Keynote: Microsoft Optics for the Cloud – A New Approach to Data Centre Technology
Schwiderski-Grosche, Scarlet
New hardware technology such as rack-scale computers (RSCs) are redefining the landscape of data center computing today, achieving both higher bandwidth and lower latency. As a basic building block of a redesigned stack of hardware, OS, storage and network, such infrastructure is increasingly suitable for HPC workloads. However, most of today’s data center technology was designed or conceived in the era before the cloud existed. Many of the technologies represent compromises, encumbered by legacy thinking. We are exploring optical technologies across the three primary resources, networking, storage, and compute. In this talk, we will outline the future challenges in the data center and discuss the limitations of current technology. We will take a holistic end-to-end view of the needs of the cloud and present our work on optical networking and storage.
 So what I will do in the next 45 minutes is take a research view on data center technologies. And that's important to emphasize. I'm not part of the Microsoft Azure product group, but I'm part of Microsoft research. So there we take a longer term view on technological development, development, so it's three to five years or even longer on the time horizon. So just keep that in mind because what I'm presenting is has nothing to do with product roadmap, although I will talk about Azure as well. And I will indeed start with a quote by our Azure CTO, Marcus Sinevich, that says, the fundamental disruption that the cloud is creating is based on instant access to compute and infinite storage. So instant access to compute and infinite storage, which is of course sort of a great vision statement, especially for the HPC community. But that's sort of the goalpost. That's what ideally we want to achieve with the cloud. But what I will show you in the next 45 minutes is that with current cloud technologies, we are approaching a wall. So why is that? Well, current cloud technologies were conceived before the cloud accesses it and they have evolved a lot and they are still evolving a lot, but they are still hindered by their legacy design. So at MSR in Cambridge, we are taking a new approach. In our research team, we are looking at new technologies for the cloud that exploit optics. And today I will motivate why we use optics and then talk about our work in optical networking and optical storage. But as we start, let's have a look at Microsoft Azure today. So today the infrastructure looks like that. We deploy data centers in more than 50 regions around the world. Each of these data centers has in the order of 10 to 20, each of these regions has in the order of 10 to 20 data centers. And each data center has in the order of 100,000 servers. All this is connected by 10,000s of kilometers of fiber. So it's a vast infrastructure indeed. And keep in mind that has grown enormously. So in the last four to five years, the growth factor has been about 10x. So that is about sort of doubling up every year. And just to give you an example here, so this is a data center that just a few years ago was sort of cutting edge and doing everything to meet customer demands. And this is a wider image where you see the extension taking place and this data center being extended to sort of cover a two mile end to end region. So massive growth really, if you can imagine our colleagues in Microsoft Azure who maintain an infrastructure like this and who basically double up every year. Of course we have an HPC offering. I will not talk about this today, but you're probably aware. I have just been given a couple of slides by our HPC colleagues and there have been some recent announcements, some new HPC offerings that we have in the company that really deliver great performance for HPC workloads. And I have this overview slides where you show the new virtual machines in the HP and HPC series that really for today and for the immediate future, of course, do everything that you need in HPC for HPC workloads. But that's not the topic of today. I will, as I said, take a research view and developments. And there I want to look back and kind of see how does Azure grow? How can it meet this sort of increase in demand? So how do we scale cloud capacity? Well, of course, we have to sort of improve the infrastructure on all levels. That means faster and faster servers, more processing power, more accelerators such as FPGAs and GPUs, and more memory. But we also have to build faster and faster networks. And so in order to meet this, it would 10X and four to five years or doubling up every year growth in the cloud infrastructure. And if this was not enough, there's actually a phenomenon that is summarized here in a slide by Mark Rosinovic that talks about overall data generation. So the amount of data generated by all of mankind is doubling every other year. So let that sink in. We have exponential growth in data, and that's overall data since the start of mankind. So that is, of course, massive. And where does this data come from? Well, you and HPC know more about this. I know an electron microscope, for example, producing terabytes of data every day. But then you have Internet of Things with all this edge data coming in, sensor data coming in from the edge. You have, of course, your consumer data, and you have machine learning and AI workloads driving this further. But how big is a zetabyte? It's kind of quite difficult to get the notion of this. So that's a hard disk, a 10 terabyte capacity, which is sort of cutting edge today. And you would need 100 million of those to get to one zetabyte. So if you stack those up in a sort of tower, that would be 2,500 kilometers high. And if you wanted to transmit that sort of data across a high end data center network link today, that would take you 2,500 years. So our colleagues in Azure are actually talking about the data tsunami. And of course, for them, it's sort of, you know, this is coming and it's sort of working to meet up today and tomorrow and next year. But it's sort of the growth is frightening. So really, with this exponential growth and data, we need, respectively, we need the exponential growth around storage, but of course, also compute and networking. And Microsoft has done a lot to build up the cloud infrastructure accordingly, as I said, by pushing existing technologies to the brink, but also by making considerable investments in custom hardware. So I have a few examples here of custom hardware that Microsoft has built. So for example, as regards compute, we are building our own rec service and contributing these designs to the open compute project in Project Olympus. Regarding storage, we're designing our own custom storage recs for cold storage. So that's a sort of infrequently accessed data in Project Pelican. And just to say that Project Pelican is a project that started almost 10 years ago at the, in Microsoft Research Cambridge, and that was built up to a level of maturity where it could be transferred into Azure and is now running at scale in Azure data centers. So that's a great, it was a great success for the Cambridge team to build up their research to something that can really be productized. And regarding networking in Project Medicine, we're designing our own fiber transmission technology to achieve multi-terrobit speeds between data centers. And finally also regarding networking, we are laying down our own subsea cables between continents in order to provide higher bandwidth and lower latency. So a lot has been achieved, a very vast infrastructure indeed, and that enabled us to sort of grow across the three dimensions. And please keep these in mind because I'll be looking back at compute storage and networking later on. But I also said it's challenging. We have the data tsunami, we're in the middle of this already, of course, and there's more disruption on the horizon. And many of you will kind of know what I'm talking about. It's the end of Moore's law. So the limitations I have with CMOS scaling, they are of course on the horizon as well. And what might be less well known is that I have the end of Moore's law in compute. So there's Moore's law, of course, saying that every other year I get processors that are twice as fast but basically at the flat cost factor. So what's less well known is that I have similar phenomena in storage and networking. There's also a similar phenomena to the end of Moore's law. And we don't know exactly when this will take place, but it's inevitable that it will because of the limitations of CMOS scaling. So all this transistor based technology kind of reaching the limit of what can be achieved. So what we know is that we need new growth curves, right? We need new technologies that give us these exponential growth because we need this because of the dated tsunami. And that of course for an organization like Microsoft Research where we have the luxury to stand back and say three to five years or even longer on the time horizon, what might these technologies be that give us this growth? That's the kind of position we are in at MSR. And so we started a few years ago, probably four, five years ago, looking at the properties that these data centers need to fulfill or these new technologies need to fulfill in order to meet this growth demand. And then looking at different options, evaluating those and we in the Cambridge lab settled on optics. There are other parts of the company where other technologies are investigated. You might have heard about the DNA storage work. So we have a team working on this. We have teams that work on accelerator technology, et cetera. So there is quite a lot going on because of course you can't just go into one direction and hope that it will work out. But in order to overcome this upcoming disruption, you have to of course look at all different possibilities. Right. So that brings me to the topic of the talk, the optics. But let's just briefly summarize. So what I basically said is that Microsoft Azure, the Microsoft cloud is an impressive infrastructure. It has grown massively over the years. But it is facing the two main challenges, the data tsunami, the doubling of data every other year, and the end of Moore's law across compute storage and networking. So we settled on optics and why is that? Well, there have been impressive advances in optical technologies in the 21st century. Just be aware that since 2005, we had four physics Nobel prizes being awarded in the space of optics. So they have been breakthrough innovations in academic labs across the world that are relevant here. And so we started about four, five years ago. We started on sort of individual project collaborations with optics labs around the world. So at the moment, this is just a number of the universities we are working with currently on projects that bring the physics side together with what's needed for the data center to explore sort of different aspects of bringing optical technologies into the cloud. And we found we have formed the optics for the cloud research alliance. So what's so special about this? Well, if you kind of look at this picture, we are now working with in the optics space. So that's really part of physics. But in order to do this work, we need to take a very cross-disciplinary approach. So we need, of course, competence and hardware storage and networking and really have to build very cross-disciplinary teams of people. And that's probably easier in an organization like Microsoft Research than in university because you can just build up the teams depending on funding, of course, but whatever you require. So we have physicists now in-house. We have, of course, computer scientists working on networking, storage technologies, engineers, hardware people, et cetera. So a very cross-disciplinary team of people looking at this from an end-to-end point of view. And we can do this because we own and operate the entire cloud stack. So for example, on the physical level, we develop our own photonic integrated circuits, so called PICS, in the lab and have them fabricated externally. And then later on, I will talk about our storage work and also our work in networking that will be up next. Okay. So let's look at optical networking for the cloud. So in this section, the next 10 to 15 minutes, I will show you that by using light and glass, we can achieve high bandwidth and ultra low latency in the data center. And I will show you how a fast tunable laser that we have designed in the Cambridge lab can fundamentally change the way in which we design networks and applications. So how does data center networking work today? So as mentioned earlier, a typical data center has in the order of 100,000 servers across a few thousand racks. So each rack has so many servers. And on the top of each rack, you have an electrical switch. And this is then connected in a hierarchy of electrical switches so that every rack and the data center can talk to every other rack. So that's what an electrical switch looks like. And it uses so-called pluggable transceivers that take optical signals and convert them into electrical, into the electrical domain, so into zeros and ones. And why is that? Because everything that is transmitted over two meters or more needs to be sent in the optical domain because electrical would not be possible due to signal degradation. So we have to use optics anyway for anything that is two meters or longer. So that means in a hierarchy like this, we need to really, sorry, pointer is this one, yeah. In a hierarchy like this, we need to send the electrical signal, then convert it to optical, then send it the next level up, convert it back to electrical, then back to optical, send it further, et cetera. So what happens is that you convert the package into the electrical domain and then in your switch you have a little microprocessor that does an address lookup, so it kind of needs to find out where the packet has to go next, which hope it has to go next. It then queues the packet at the respective output port. And then my pluggable transceiver converts it back into optical and sends it on. So it's quite complicated because depending on which racks you send your packet to and from, you can have up to five conversions or so between optical, electrical, back and forth. So that is, of course, quite an overhead and that's what's happening in the data center, but that's also the technology that's used in the Internet. And why, I mean, that does look fairly complicated. So why have we settled on this? Why have we been okay? Well because Moore's law for networking is valid still and what this means is basically we get new switch technology every other year that gives us the increase in performance while keeping a flat cost factor. So we kind of more or less get this exponential growth automatically with a technological advance of CMOS technologies. So flat cost factor, which is great because our colleagues in Microsoft Azure don't have to give it that much thought today because they know new switch chips are coming very soon and give them the growth that they need to kind of, to manage the workloads that the customer delivers. Okay, so I mentioned this beforehand, of course. So we have this, the electrical switching exponential growth, but it is tailing off. And this means the tailing off means that we might have new CMOS technologies giving us electrical switches, but they might be more expensive and more expensive with every generation that is coming. So every two, three years when we change the equipment in the data center, we have to pay more, which would give us, gives us an exponential growth in cost factor as well, which of course will not be very popular with the customer. So we instead are looking at optical switching to overcome these limitations. So why optics? Well, we know that we already use optics in the data center with all these conversions for the transmissions across our fibers. So if you kind of think back, we had this fairly complicated hierarchy of electrical switches and the idea would be to kind of try to find some electrical, some optical switch instead. And another reason is that optical technologies are not, are not affected by the limitations of CMOS scaling. So they are most more likely to give us this sort of natural exponential growth with new technology advances. But how can we switch packets using light? I will show you an animation in a couple of minutes. That basically shows that we have two main components that we want to use here. And the idea, it's a very basic, simple idea is that basically for every destination rack that we want to reach, we use a respective wavelength in the optical spectrum. So basically a color. So let's say I want to send a packet to the red rack, then I send it in the red wavelength, green, in the green wavelengths, et cetera. Now do this by using this optical switch that we call, so I start the video now, the so-called arrayed waveguide grating router. So this is a kind of conceptual. And white light is broken into respective wavelengths. And you see red is directed to a specific port, green is directed to a specific port. And that it's basically a passive component. It's grating on glass like a prison technology that uses one respective wavelength for each rack in the data center. And here you see this sort of how we realize it on our photonic integrated circuit. So that's my optical switch, the arrayed waveguide grating router, AWGR. And here you see another component. And that's a tunable laser because of course what we now need is a laser that doesn't give us white light, but that gives us a respective wavelength so that we can encode our packet and the respective color, send it to our optical switch and then have it routed automatically to the receiving rack. So a few years ago we started to look at this tunable laser technology. And we started of course with commodity off the shelf tunable lasers that you can buy at your supplier. So we found that tuning the laser between one wavelength and another took one millisecond. There are people who know about networking know this as a lifetime in networking terms, so absolutely unacceptable. What the team in Cambridge then did is take this sort of commodity off the shelf tunable laser but kind of squeeze every bit of performance out of it that we can by integrating into this board, by designing our own algorithm for tunability, et cetera. Here's one of the prototypes we produced. And we were able to reduce the switching time by four orders of magnitude to 100 nanosecond. Well that sounds great. I mean that's an amazing achievement, right? If you kind of look at this research achievements that have been made. But let's have a look at, you know, a specific example. So let's say we want to send a packet, 256 byte packet to the red rack. And we want to send another packet to the green rack straight afterwards. What would happen now is that basically because of the switching time, I have this delay of 100 nanoseconds in between. I just have to wait before I can send the next packet, right? That means I have a link utilization of less than 30%, which is not acceptable. Again, the overhead is too big. So this didn't work. And that means in MSR Cambridge, we decided to take a fundamentally different approach and design our own photonic integrated circuit, our own PIC. And this is sort of what it's sketched today. You see here, of course, the device that you've seen earlier in the animation. So that's the AWGR, the optical switch. And then on the left-hand side, you basically have lots of little lasers that produce the respective wavelengths. And you can, in principle, go up to hundreds of different wavelengths. So here is the size of the PIC in comparison to a pound coin. And here's basically what happens. So let's assume you're looking back at red and green packets. So the one side of this chip then creates all these different wavelengths. And the other side is basically like a wavelength filter. So what we see here is that when the filter is on green, it only lets through the green wavelengths. If we tune it on red, it only lets through the red wavelengths. And in this way, I can, in principle, use hundreds of different wavelengths to reach every rack in my data center. So this is a device that we designed in the Cambridge lab and that was then fabricated in an external foundry. So now let's look back at our performance, our switching performance, as mentioned before. So how fast can we now switch our tunable laser? Well, it's less than one nanosecond, which is amazing because if we look at our example, then our switching time is almost negligible and I reach almost full link utilization. So, we talked about rack to rack. So let's have a quick look at how that would work with between racks across the whole data center. So basically at the top of each rack, we now have our tunable laser that produces, creates a packet in the respective wavelengths depending on where the packet is going. We don't need plugable transceivers and we don't need this complicated hierarchy of electrical switches. We just convert in the rack our signal into the optical domain and then send it across our passive component about across our AWGR, where it is rooted depending on wavelengths to the destination rack. So now we have the effect that we have optical technology that is not affected by CMOS, by the end of Moore's law, and we can hopefully keep a flat cost factor there. And what we also achieve, and there's of course, I mean, it's a fundamentally new approach to networking. So there is a lot of consequences this has on what's happening today, of course. But if you kind of consider it at the moment, you send packets and you have to buffer them and there is congestion, et cetera. That's of course a big problem in data center networks, where it's now what we are saying is that we send a packet from one rack to another basically at the speed of light. So we know exactly how long the packet will take, in this case 250 nanoseconds, in order to get to the destination rack. And that depends on the length of the distance, on the distance between the racks. So we basically now have a deterministic approach. So we know exactly how long it will take, and that is for applications in HPC, for example, this of course is a great opportunity, because we can rely on this and don't have to plan in arbitrary times of packet processing and buffering. But just to say, so there are, of course, it's a fundamentally new approach. So there are lots of technical challenges. And we have, so we started this project probably even further than that, but about in 2016, we developed this sort of our own version of the board with custom tunable laser. And our own AWGR component that was sort of last year. Then there's another big problem now, because we don't have buffers. We need to synchronize clocks very exactly, but in the different racks of the data center. So we've been able to make impressive advances there last year. And the team is basically now very confident that we can overcome these very difficult problems and one day use optical networks and data center. But just to make clear, this is a very long term view in order to develop such a technology so that it can run at scale and stable and within certain cost factors in the data center. That's of course a huge leap. So that will take years to get there. But yeah, I mean, a few years ago, we didn't kind of think that we would solve these important problems and they have been solved. So there is a lot of optimism with this networking work to go further. Okay, so that was the project introduction to our work in optical networking. And what I will show you next is the work we do in optical storage. And just to say, so with this storage, we're looking at so-called cold or frozen data. So this is archival data that is accessed very infrequently or possibly never. Think about your health insurance data from like 30 years ago. You can't throw it away, but you have to save it somewhere. And the idea here is to develop a storage media from the ground up specifically for the data center. So we were not sort of threw away everything that existed before and really looked from scratch what are the requirements in the data center and how can we meet it. So I brought a little glass sample here. It's basically, it's like three centimeters or so. And what you see there is basically the data is encoded in these little blue dots. And that's encoded inside the bulk of the glass. There are of course other optical storage media like DVDs, Blu-rays, etc. But they are surface technologies. Here my data is encoded inside the bulk of the glass. And this is just the Azure logo. So going back to our dated tsunami, right? Overall data generated growing exponentially. With the dated tsunami, we can, we know that with existing storage technologies, the ratio of data we can afford to store goes down because the existing technologies cannot keep up. I've got a couple of slides of that. I could sort of talk about this at great lengths, but the capacity is not high enough. And the problem is that the lifetime is not high enough either. So this ratio goes down. And what we are trying with Project Silica is to push this curve up so that we can afford to store more data or that we can store data for longer depending on our workloads. So let's look at what we have today. Of course, Azure has offerings across all the different data storage requirements. And you talk about hot data to code to frozen data. So there are current, great running Azure offerings. On the right-hand side, you see the existing technologies that are used. And some people might be surprised to hear that tape is still being used quite extensively in data centers. It's actually quite a good technology. Another big problem is that the spooling takes a long time because these tapes can be like a kilometer long. So our Silica project that sits down there with the code to frozen data with the goal to basically eternally store your data and ideally not have to back it up again. But eternal lifetime of the storage medium. So just very quickly, what are the existing archival storage media? So this is a summary. On the left-hand side, we basically have the magnetic ones, hard disk and magnetic tape. And here the optical ones, optical disks like Blu-ray, DVDs. The problem is with all of these that there are surface technologies. So they suffer from things like it's called bit rot. It's one thing or electromagnetic pulses can affect the data or just electrons leak out. So none of these storage technologies that these existing ones lasts more than 10 years. The longest lifetime is for magnetic tape and that's 10-year lifetime. My hard disk and my optical disk has a lower lifetime. So if you think back at the one zetabyte, if I have the 100 million hard disks, but it takes me three years to copy the data onto my 100 million hard disks, and then at the end of that time, I need another three years to back it up to new storage media, then effectively I might have only two or three years left where the data is actually stored before I have to revisit this. So that doesn't scale very well. So we took inspiration by this and this is basically a clay, a bit of clay, a clay tablet that's in the British Museum. It's 5,000 years old and it's one of the oldest bits of data that we have. And of course it's carving in the clay. So it's a physical change in the material. And that's basically the motivation for us using silica glass. What we want to achieve is that we basically write the data into the storage media once and then leave it there forever. So we want basically thousands of years lifetime. And we don't want these effects that we have with current media. We can't accept it in our technology. And we of course need a cheap material as well. So there's quite a lot of requirements. And what we settled on is basically it's silicon dioxide. It's just basically it's glass. It's like melted sand. So it's a very cheap material. And I'll show you regarding robustness. We tested it. It's one of our researchers doing some tests at home. It's quite an amateur to be here of course. But that's our colleague as a physics scientist, Ariel, who did these tests. And indeed we were able to read the data without a problem afterwards because the data is stored inside the glass. So and you might say why is this possible? Now why was it not possible earlier? Well it's because now we have the femtosecond laser technology. So it's a laser basically that pulses in approximately 100 femtosecond wavelengths. And how much is a femtosecond? 10 to the power of minus 15 seconds. So very, very, very short. So the idea with this femtosecond laser is that you basically direct the laser beam through a lens inside the bulk of the silica glass. And what happens then is that a voxel is created. That's a three-dimensional little structure. And if we kind of look back to the time before the femtosecond laser, we had the picosecond laser, and what you see here on a larger scale of 50 nanometers, you see that cracks form in the glass. So basically it's sort of the wavelength is not short enough. It's not a you can't read any data out of this. But with the femtosecond laser, and now we are looking at 10 nanometers, we get really very well-defined voxels in our glass. And you saw earlier, I mean, there were just a number of probably 10 of these blocks of arrays of dots in this glass, which is not putting it to maximum capacity, but it's all very, very dense. And this has enabled us to design this technology and project silica. So how do you store the data in glass? How are these voxels formed? So basically what happens is that depending on the pulse and the polarization of your laser beam, the voxels have a certain volume, a size, x, y, yz direction, the volume, but they also have these sort of nano gratings inside the glass. So we're working with this with the University of Southampton, and there have been relevant findings in the early 2003 that basically you get these nano gratings within the voxel. So basically you can say it's the 3D position of the voxel inside the glass, and then the size of the voxel, and the orientation of the nano gratings, where we encode the bits, basically. Okay, in the one point I want to make, I will finish with an animation, but just one point to make, the good thing with this storage technology is that we can have the right store and read completely disaggregated. That means I have my right system somewhere and write my data in glass, then store it in a glass library, and then move it to the read system as and when I need it. For example, with hard disk, you basically get the read and write heads as part of the package. So here I can design it depending on how much read I need for my data. Okay, and I'll skip over those. There are just some schematic things about the write and the read process, but they're basically summarized in this animation that we've done that shows here the write process with my femtosecond laser burning these voxels, or not burning it sort of the stage beforehand, very small voxels into the bulk of the glass. And in this way, we can form a 3D, three-dimensional array of voxels inside the glass, and have lots of these little arrays and one glass, little glass square. And then once it's written, we put it in our glass library, and then later on read it out with some high definition cameras, and basically what happens is we just take an image layer by layer in these arrays, and go through top to bottom, take a picture of each of these layers, and then use computer vision techniques to read out the data. Okay, so that brings me to the end of my talk to quickly summarize what I did. I talked about Azure, about the amazing growth that Azure is undergoing, and how our colleagues in the product group have managed this growth. But I then also talked about the dated tsunami and the problems with the end of Moore's law and compute networking and storage, and how we with our optical technologies hope to overcome those. And I introduced two specific projects in the space of networking and storage, and we have other projects going on, and looking at how it's going. Now, we are very hopeful that these technologies one day will run in the data center. And just to point you to a few upcoming opportunities, and we have a website around our theme of line of research, opticsforthecloud.com. So just go there, and colleagues from HPC Group will be in Frankfurt next week. We also have an increasing presence in the physics and physics conferences and events with our work, because of course we are very keen to get other physicists and choose to apply their optics work in the data center. Okay, thank you very much. So many thanks Scarlet for this presentation. Indeed, about beyond the exascale, so you have seen things from femto meters up to set-up bytes. So for the sake of time, please, if there's one or two short questions, now would be a good time to address them to Scarlet. So thank you for your nice presentation. You addressed how to address a rack with the color, but what happens if several racks are talking to the same rack in the same color? Yeah, that's a good point. There is actually one part of my presentation that will have been about this, but I had to cut it out for time reasons. So this will require very careful scheduling of packets inside the data center. So a really different approach, because you can't just fire packets off and hope they arrive, because they might interfere with each other. So you need a very, you need sort of an overall scheduling approach, and that's why we have with the time synchronization, why it has been very important for us to get to a nanosecond scale time synchronization, so that we can make sure that we fire the packets off so that they don't interfere with each other. Hello? Yeah, okay. So maybe you said already, but how much data can you store on one of these little glasses, or how much you plan to store? Yeah, that question always comes up. So there are some things I can't talk about for competitive reasons, but just one observation. We are, of course, doing this work in order to land in the data center. So the rates are competitive. So if you look at the magnetic and other optics storage media, we have sort of very strict goals that we need to achieve, and we are on track. So it's certainly a competitive volume of data that we can store on there, but that's all I can say. Okay, so one last short question. So going back to the optical switching, do you need to have a different wavelength of light for each destination rack? And so what happens if you have a thousand different frequencies with a thousand different racks, and all of a sudden you double your size of your data center? Do you need to re-encode everything, and all of a sudden you need 2,000 different frequencies? Yeah, I mean, typically the data center is, of course, you have these small data centers and regions, et cetera, so there are a certain size, and you'd rather build more data centers in the region so you can scale it accordingly. But for the number of racks that are on average there today and in the foreseeable future, we have enough wavelengths to cover those. But you would need, yeah, you do need to make sure to have enough wavelengths to get to each one, but the team is very comfortable that this is no problem. So cut here, because we have a 15, like less than 15 minutes quick coffee break, which is kindly sponsored by Microsoft, so we no refresh ourselves for the hard morning we're going to face. So thanks for showing up that early. And so Scarlett said you're around for a little longer, so like more questions can be addressed to her during the break. So see you in a bit. Thank you.
 So what I will do in the next 45 minutes is take a research view on data center technologies.  And that's important to emphasize.  I'm not part of the Microsoft Azure product group, but I'm part of Microsoft research.  So there we take a longer term view on technological development, development, so it's three to  five years or even longer on the time horizon.  So just keep that in mind because what I'm presenting is has nothing to do with product  roadmap, although I will talk about Azure as well.  And I will indeed start with a quote by our Azure CTO, Marcus Sinevich, that says, the  fundamental disruption that the cloud is creating is based on instant access to compute and  infinite storage.  So instant access to compute and infinite storage, which is of course sort of a great  vision statement, especially for the HPC community.  But that's sort of the goalpost.  That's what ideally we want to achieve with the cloud.  But what I will show you in the next 45 minutes is that with current cloud technologies, we  are approaching a wall.  So why is that?  Well, current cloud technologies were conceived before the cloud accesses it and they have  evolved a lot and they are still evolving a lot, but they are still hindered by their  legacy design.  So at MSR in Cambridge, we are taking a new approach.  In our research team, we are looking at new technologies for the cloud that exploit optics.  And today I will motivate why we use optics and then talk about our work in optical networking  and optical storage.  But as we start, let's have a look at Microsoft Azure today.  So today the infrastructure looks like that.  We deploy data centers in more than 50 regions around the world.  Each of these data centers has in the order of 10 to 20, each of these regions has in  the order of 10 to 20 data centers.  And each data center has in the order of 100,000 servers.  All this is connected by 10,000s of kilometers of fiber.  So it's a vast infrastructure indeed.  And keep in mind that has grown enormously.  So in the last four to five years, the growth factor has been about 10x.  So that is about sort of doubling up every year.  And just to give you an example here, so this is a data center that just a few years ago  was sort of cutting edge and doing everything to meet customer demands.  And this is a wider image where you see the extension taking place and this data center  being extended to sort of cover a two mile end to end region.  So massive growth really, if you can imagine our colleagues in Microsoft Azure who maintain  an infrastructure like this and who basically double up every year.  Of course we have an HPC offering.  I will not talk about this today, but you're probably aware.  I have just been given a couple of slides by our HPC colleagues and there have been  some recent announcements, some new HPC offerings that we have in the company that really deliver  great performance for HPC workloads.  And I have this overview slides where you show the new virtual machines in the HP and  HPC series that really for today and for the immediate future, of course, do everything  that you need in HPC for HPC workloads.  But that's not the topic of today.  I will, as I said, take a research view and developments.  And there I want to look back and kind of see how does Azure grow?  How can it meet this sort of increase in demand?  So how do we scale cloud capacity?  Well, of course, we have to sort of improve the infrastructure on all levels.  That means faster and faster servers, more processing power, more accelerators such  as FPGAs and GPUs, and more memory.  But we also have to build faster and faster networks.  And so in order to meet this, it would 10X and four to five years or doubling up every  year growth in the cloud infrastructure.  And if this was not enough, there's actually a phenomenon that is summarized here in a  slide by Mark Rosinovic that talks about overall data generation.  So the amount of data generated by all of mankind is doubling every other year.  So let that sink in.  We have exponential growth in data, and that's overall data since the start of mankind.  So that is, of course, massive.  And where does this data come from?  Well, you and HPC know more about this.  I know an electron microscope, for example, producing terabytes of data every day.  But then you have Internet of Things with all this edge data coming in, sensor data  coming in from the edge.  You have, of course, your consumer data, and you have machine learning and AI workloads  driving this further.  But how big is a zetabyte?  It's kind of quite difficult to get the notion of this.  So that's a hard disk, a 10 terabyte capacity, which is sort of cutting edge today.  And you would need 100 million of those to get to one zetabyte.  So if you stack those up in a sort of tower, that would be 2,500 kilometers high.  And if you wanted to transmit that sort of data across a high end data center network  link today, that would take you 2,500 years.  So our colleagues in Azure are actually talking about the data tsunami.  And of course, for them, it's sort of, you know, this is coming and it's sort of working  to meet up today and tomorrow and next year.  But it's sort of the growth is frightening.  So really, with this exponential growth and data, we need, respectively, we need the exponential  growth around storage, but of course, also compute and networking.  And Microsoft has done a lot to build up the cloud infrastructure accordingly, as I said,  by pushing existing technologies to the brink, but also by making considerable investments  in custom hardware.  So I have a few examples here of custom hardware that Microsoft has built.  So for example, as regards compute, we are building our own rec service and contributing  these designs to the open compute project in Project Olympus.  Regarding storage, we're designing our own custom storage recs for cold storage.  So that's a sort of infrequently accessed data in Project Pelican.  And just to say that Project Pelican is a project that started almost 10 years ago at  the, in Microsoft Research Cambridge, and that was built up to a level of maturity where  it could be transferred into Azure and is now running at scale in Azure data centers.  So that's a great, it was a great success for the Cambridge team to build up their research  to something that can really be productized.  And regarding networking in Project Medicine, we're designing our own fiber transmission  technology to achieve multi-terrobit speeds between data centers.  And finally also regarding networking, we are laying down our own subsea cables between  continents in order to provide higher bandwidth and lower latency.  So a lot has been achieved, a very vast infrastructure indeed, and that enabled us to sort of grow  across the three dimensions.  And please keep these in mind because I'll be looking back at compute storage and networking  later on.  But I also said it's challenging.  We have the data tsunami, we're in the middle of this already, of course, and there's more  disruption on the horizon.  And many of you will kind of know what I'm talking about.  It's the end of Moore's law.  So the limitations I have with CMOS scaling, they are of course on the horizon as well.  And what might be less well known is that I have the end of Moore's law in compute.  So there's Moore's law, of course, saying that every other year I get processors that  are twice as fast but basically at the flat cost factor.  So what's less well known is that I have similar phenomena in storage and networking.  There's also a similar phenomena to the end of Moore's law.  And we don't know exactly when this will take place, but it's inevitable that it will because  of the limitations of CMOS scaling.  So all this transistor based technology kind of reaching the limit of what can be achieved.  So what we know is that we need new growth curves, right?  We need new technologies that give us these exponential growth because we need this because  of the dated tsunami.  And that of course for an organization like Microsoft Research where we have the luxury  to stand back and say three to five years or even longer on the time horizon, what might  these technologies be that give us this growth?  That's the kind of position we are in at MSR.  And so we started a few years ago, probably four, five years ago, looking at the properties  that these data centers need to fulfill or these new technologies need to fulfill in  order to meet this growth demand.  And then looking at different options, evaluating those and we in the Cambridge lab settled  on optics.  There are other parts of the company where other technologies are investigated.  You might have heard about the DNA storage work.  So we have a team working on this.  We have teams that work on accelerator technology, et cetera.  So there is quite a lot going on because of course you can't just go into one direction  and hope that it will work out.  But in order to overcome this upcoming disruption, you have to of course look at all different  possibilities.  Right.  So that brings me to the topic of the talk, the optics.  But let's just briefly summarize.  So what I basically said is that Microsoft Azure, the Microsoft cloud is an impressive  infrastructure.  It has grown massively over the years.  But it is facing the two main challenges, the data tsunami, the doubling of data every  other year, and the end of Moore's law across compute storage and networking.  So we settled on optics and why is that?  Well, there have been impressive advances in optical technologies in the 21st century.  Just be aware that since 2005, we had four physics Nobel prizes being awarded in the  space of optics.  So they have been breakthrough innovations in academic labs across the world that are  relevant here.  And so we started about four, five years ago.  We started on sort of individual project collaborations with optics labs around the world.  So at the moment, this is just a number of the universities we are working with currently  on projects that bring the physics side together with what's needed for the data center to  explore sort of different aspects of bringing optical technologies into the cloud.  And we found we have formed the optics for the cloud research alliance.  So what's so special about this?  Well, if you kind of look at this picture, we are now working with in the optics space.  So that's really part of physics.  But in order to do this work, we need to take a very cross-disciplinary approach.  So we need, of course, competence and hardware storage and networking and really have to  build very cross-disciplinary teams of people.  And that's probably easier in an organization like Microsoft Research than in university  because you can just build up the teams depending on funding, of course, but whatever you require.  So we have physicists now in-house.  We have, of course, computer scientists working on networking, storage technologies, engineers,  hardware people, et cetera.  So a very cross-disciplinary team of people looking at this from an end-to-end point of  view.  And we can do this because we own and operate the entire cloud stack.  So for example, on the physical level, we develop our own photonic integrated circuits,  so called PICS, in the lab and have them fabricated externally.  And then later on, I will talk about our storage work and also our work in networking that  will be up next.  Okay.  So let's look at optical networking for the cloud.  So in this section, the next 10 to 15 minutes, I will show you that by using light and glass,  we can achieve high bandwidth and ultra low latency in the data center.  And I will show you how a fast tunable laser that we have designed in the Cambridge lab  can fundamentally change the way in which we design networks and applications.  So how does data center networking work today?  So as mentioned earlier, a typical data center has in the order of 100,000 servers across  a few thousand racks.  So each rack has so many servers.  And on the top of each rack, you have an electrical switch.  And this is then connected in a hierarchy of electrical switches so that every rack  and the data center can talk to every other rack.  So that's what an electrical switch looks like.  And it uses so-called pluggable transceivers that take optical signals and convert them  into electrical, into the electrical domain, so into zeros and ones.  And why is that?  Because everything that is transmitted over two meters or more needs to be sent in the  optical domain because electrical would not be possible due to signal degradation.  So we have to use optics anyway for anything that is two meters or longer.  So that means in a hierarchy like this, we need to really, sorry, pointer is this one,  yeah.  In a hierarchy like this, we need to send the electrical signal, then convert it to  optical, then send it the next level up, convert it back to electrical, then back to  optical, send it further, et cetera.  So what happens is that you convert the package into the electrical domain and then in your  switch you have a little microprocessor that does an address lookup, so it kind of needs  to find out where the packet has to go next, which hope it has to go next.  It then queues the packet at the respective output port.  And then my pluggable transceiver converts it back into optical and sends it on.  So it's quite complicated because depending on which racks you send your packet to and  from, you can have up to five conversions or so between optical, electrical, back and  forth.  So that is, of course, quite an overhead and that's what's happening in the data center,  but that's also the technology that's used in the Internet.  And why, I mean, that does look fairly complicated.  So why have we settled on this?  Why have we been okay?  Well because Moore's law for networking is valid still and what this means is basically  we get new switch technology every other year that gives us the increase in performance  while keeping a flat cost factor.  So we kind of more or less get this exponential growth automatically with a technological advance  of CMOS technologies.  So flat cost factor, which is great because our colleagues in Microsoft Azure don't have  to give it that much thought today because they know new switch chips are coming very  soon and give them the growth that they need to kind of, to manage the workloads that the  customer delivers.  Okay, so I mentioned this beforehand, of course.  So we have this, the electrical switching exponential growth, but it is tailing off.  And this means the tailing off means that we might have new CMOS technologies giving  us electrical switches, but they might be more expensive and more expensive with every  generation that is coming.  So every two, three years when we change the equipment in the data center, we have to  pay more, which would give us, gives us an exponential growth in cost factor as well,  which of course will not be very popular with the customer.  So we instead are looking at optical switching to overcome these limitations.  So why optics?  Well, we know that we already use optics in the data center with all these conversions  for the transmissions across our fibers.  So if you kind of think back, we had this fairly complicated hierarchy of electrical switches  and the idea would be to kind of try to find some electrical, some optical switch instead.  And another reason is that optical technologies are not, are not affected by the limitations  of CMOS scaling.  So they are most more likely to give us this sort of natural exponential growth with new  technology advances.  But how can we switch packets using light?  I will show you an animation in a couple of minutes.  That basically shows that we have two main components that we want to use here.  And the idea, it's a very basic, simple idea is that basically for every destination rack  that we want to reach, we use a respective wavelength in the optical spectrum.  So basically a color.  So let's say I want to send a packet to the red rack, then I send it in the red wavelength,  green, in the green wavelengths, et cetera.  Now do this by using this optical switch that we call, so I start the video now, the so-called  arrayed waveguide grating router.  So this is a kind of conceptual.  And white light is broken into respective wavelengths.  And you see red is directed to a specific port, green is directed to a specific port.  And that it's basically a passive component.  It's grating on glass like a prison technology that uses one respective wavelength for each  rack in the data center.  And here you see this sort of how we realize it on our photonic integrated circuit.  So that's my optical switch, the arrayed waveguide grating router, AWGR.  And here you see another component.  And that's a tunable laser because of course what we now need is a laser that doesn't give  us white light, but that gives us a respective wavelength so that we can encode our packet  and the respective color, send it to our optical switch and then have it routed automatically  to the receiving rack.  So a few years ago we started to look at this tunable laser technology.  And we started of course with commodity off the shelf tunable lasers that you can buy  at your supplier.  So we found that tuning the laser between one wavelength and another took one millisecond.  There are people who know about networking know this as a lifetime in networking terms,  so absolutely unacceptable.  What the team in Cambridge then did is take this sort of commodity off the shelf tunable  laser but kind of squeeze every bit of performance out of it that we can by integrating into  this board, by designing our own algorithm for tunability, et cetera.  Here's one of the prototypes we produced.  And we were able to reduce the switching time by four orders of magnitude to 100 nanosecond.  Well that sounds great.  I mean that's an amazing achievement, right?  If you kind of look at this research achievements that have been made.  But let's have a look at, you know, a specific example.  So let's say we want to send a packet, 256 byte packet to the red rack.  And we want to send another packet to the green rack straight afterwards.  What would happen now is that basically because of the switching time, I have this delay of  100 nanoseconds in between.  I just have to wait before I can send the next packet, right?  That means I have a link utilization of less than 30%, which is not acceptable.  Again, the overhead is too big.  So this didn't work.  And that means in MSR Cambridge, we decided to take a fundamentally different approach  and design our own photonic integrated circuit, our own PIC.  And this is sort of what it's sketched today.  You see here, of course, the device that you've seen earlier in the animation.  So that's the AWGR, the optical switch.  And then on the left-hand side, you basically have lots of little lasers that produce the  respective wavelengths.  And you can, in principle, go up to hundreds of different wavelengths.  So here is the size of the PIC in comparison to a pound coin.  And here's basically what happens.  So let's assume you're looking back at red and green packets.  So the one side of this chip then creates all these different wavelengths.  And the other side is basically like a wavelength filter.  So what we see here is that when the filter is on green, it only lets through the green  wavelengths.  If we tune it on red, it only lets through the red wavelengths.  And in this way, I can, in principle, use hundreds of different wavelengths to reach  every rack in my data center.  So this is a device that we designed in the Cambridge lab and that was then fabricated  in an external foundry.  So now let's look back at our performance, our switching performance, as mentioned before.  So how fast can we now switch our tunable laser?  Well, it's less than one nanosecond, which is amazing because if we look at our example,  then our switching time is almost negligible and I reach almost full link utilization.  So, we talked about rack to rack.  So let's have a quick look at how that would work with between racks across the whole data  center.  So basically at the top of each rack, we now have our tunable laser that produces, creates  a packet in the respective wavelengths depending on where the packet is going.  We don't need plugable transceivers and we don't need this complicated hierarchy of  electrical switches.  We just convert in the rack our signal into the optical domain and then send it across  our passive component about across our AWGR, where it is rooted depending on wavelengths  to the destination rack.  So now we have the effect that we have optical technology that is not affected by CMOS, by  the end of Moore's law, and we can hopefully keep a flat cost factor there.  And what we also achieve, and there's of course, I mean, it's a fundamentally new approach  to networking.  So there is a lot of consequences this has on what's happening today, of course.  But if you kind of consider it at the moment, you send packets and you have to buffer them  and there is congestion, et cetera.  That's of course a big problem in data center networks, where it's now what we are saying  is that we send a packet from one rack to another basically at the speed of light.  So we know exactly how long the packet will take, in this case 250 nanoseconds, in order  to get to the destination rack.  And that depends on the length of the distance, on the distance between the racks.  So we basically now have a deterministic approach.  So we know exactly how long it will take, and that is for applications in HPC, for example,  this of course is a great opportunity, because we can rely on this and don't have to plan  in arbitrary times of packet processing and buffering.  But just to say, so there are, of course, it's a fundamentally new approach.  So there are lots of technical challenges.  And we have, so we started this project probably even further than that, but about in 2016,  we developed this sort of our own version of the board with custom tunable laser.  And our own AWGR component that was sort of last year.  Then there's another big problem now, because we don't have buffers.  We need to synchronize clocks very exactly, but in the different racks of the data center.  So we've been able to make impressive advances there last year.  And the team is basically now very confident that we can overcome these very difficult  problems and one day use optical networks and data center.  But just to make clear, this is a very long term view in order to develop such a technology  so that it can run at scale and stable and within certain cost factors in the data center.  That's of course a huge leap.  So that will take years to get there.  But yeah, I mean, a few years ago, we didn't kind of think that we would solve these important  problems and they have been solved.  So there is a lot of optimism with this networking work to go further.  Okay, so that was the project introduction to our work in optical networking.  And what I will show you next is the work we do in optical storage.  And just to say, so with this storage, we're looking at so-called cold or frozen data.  So this is archival data that is accessed very infrequently or possibly never.  Think about your health insurance data from like 30 years ago.  You can't throw it away, but you have to save it somewhere.  And the idea here is to develop a storage media from the ground up specifically for  the data center.  So we were not sort of threw away everything that existed before and really looked from  scratch what are the requirements in the data center and how can we meet it.  So I brought a little glass sample here.  It's basically, it's like three centimeters or so.  And what you see there is basically the data is encoded in these little blue dots.  And that's encoded inside the bulk of the glass.  There are of course other optical storage media like DVDs, Blu-rays, etc.  But they are surface technologies.  Here my data is encoded inside the bulk of the glass.  And this is just the Azure logo.  So going back to our dated tsunami, right?  Overall data generated growing exponentially.  With the dated tsunami, we can, we know that with existing storage technologies, the ratio  of data we can afford to store goes down because the existing technologies cannot keep up.  I've got a couple of slides of that.  I could sort of talk about this at great lengths, but the capacity is not high enough.  And the problem is that the lifetime is not high enough either.  So this ratio goes down.  And what we are trying with Project Silica is to push this curve up so that we can afford  to store more data or that we can store data for longer depending on our workloads.  So let's look at what we have today.  Of course, Azure has offerings across all the different data storage requirements.  And you talk about hot data to code to frozen data.  So there are current, great running Azure offerings.  On the right-hand side, you see the existing technologies that are used.  And some people might be surprised to hear that tape is still being used quite extensively  in data centers.  It's actually quite a good technology.  Another big problem is that the spooling takes a long time because these tapes can be like  a kilometer long.  So our Silica project that sits down there with the code to frozen data with the goal  to basically eternally store your data and ideally not have to back it up again.  But eternal lifetime of the storage medium.  So just very quickly, what are the existing archival storage media?  So this is a summary.  On the left-hand side, we basically have the magnetic ones, hard disk and magnetic tape.  And here the optical ones, optical disks like Blu-ray, DVDs.  The problem is with all of these that there are surface technologies.  So they suffer from things like it's called bit rot.  It's one thing or electromagnetic pulses can affect the data or just electrons leak out.  So none of these storage technologies that these existing ones lasts more than 10 years.  The longest lifetime is for magnetic tape and that's 10-year lifetime.  My hard disk and my optical disk has a lower lifetime.  So if you think back at the one zetabyte, if I have the 100 million hard disks, but  it takes me three years to copy the data onto my 100 million hard disks, and then at the  end of that time, I need another three years to back it up to new storage media, then effectively  I might have only two or three years left where the data is actually stored before I  have to revisit this.  So that doesn't scale very well.  So we took inspiration by this and this is basically a clay, a bit of clay, a clay tablet  that's in the British Museum.  It's 5,000 years old and it's one of the oldest bits of data that we have.  And of course it's carving in the clay.  So it's a physical change in the material.  And that's basically the motivation for us using silica glass.  What we want to achieve is that we basically write the data into the storage media once  and then leave it there forever.  So we want basically thousands of years lifetime.  And we don't want these effects that we have with current media.  We can't accept it in our technology.  And we of course need a cheap material as well.  So there's quite a lot of requirements.  And what we settled on is basically it's silicon dioxide.  It's just basically it's glass.  It's like melted sand.  So it's a very cheap material.  And I'll show you regarding robustness.  We tested it.  It's one of our researchers doing some tests at home.  It's quite an amateur to be here of course.  But that's our colleague as a physics scientist, Ariel, who did these tests.  And indeed we were able to read the data without a problem afterwards because the data is stored  inside the glass.  So and you might say why is this possible?  Now why was it not possible earlier?  Well it's because now we have the femtosecond laser technology.  So it's a laser basically that pulses in approximately 100 femtosecond wavelengths.  And how much is a femtosecond?  10 to the power of minus 15 seconds.  So very, very, very short.  So the idea with this femtosecond laser is that you basically direct the laser beam  through a lens inside the bulk of the silica glass.  And what happens then is that a voxel is created.  That's a three-dimensional little structure.  And if we kind of look back to the time before the femtosecond laser, we had the picosecond  laser, and what you see here on a larger scale of 50 nanometers, you see that cracks form  in the glass.  So basically it's sort of the wavelength is not short enough.  It's not a you can't read any data out of this.  But with the femtosecond laser, and now we are looking at 10 nanometers, we get really  very well-defined voxels in our glass.  And you saw earlier, I mean, there were just a number of probably 10 of these blocks of  arrays of dots in this glass, which is not putting it to maximum capacity, but it's all  very, very dense.  And this has enabled us to design this technology and project silica.  So how do you store the data in glass?  How are these voxels formed?  So basically what happens is that depending on the pulse and the polarization of your  laser beam, the voxels have a certain volume, a size, x, y, yz direction, the volume, but  they also have these sort of nano gratings inside the glass.  So we're working with this with the University of Southampton, and there have been relevant  findings in the early 2003 that basically you get these nano gratings within the voxel.  So basically you can say it's the 3D position of the voxel inside the glass, and then the  size of the voxel, and the orientation of the nano gratings, where we encode the bits,  basically.  Okay, in the one point I want to make, I will finish with an animation, but just one point  to make, the good thing with this storage technology is that we can have the right store  and read completely disaggregated.  That means I have my right system somewhere and write my data in glass, then store it in  a glass library, and then move it to the read system as and when I need it.  For example, with hard disk, you basically get the read and write heads as part of the  package.  So here I can design it depending on how much read I need for my data.  Okay, and I'll skip over those.  There are just some schematic things about the write and the read process, but they're  basically summarized in this animation that we've done that shows here the write process  with my femtosecond laser burning these voxels, or not burning it sort of the stage beforehand,  very small voxels into the bulk of the glass.  And in this way, we can form a 3D, three-dimensional array of voxels inside the glass, and have  lots of these little arrays and one glass, little glass square.  And then once it's written, we put it in our glass library, and then later on read it out  with some high definition cameras, and basically what happens is we just take an image layer  by layer in these arrays, and go through top to bottom, take a picture of each of these  layers, and then use computer vision techniques to read out the data.  Okay, so that brings me to the end of my talk to quickly summarize what I did.  I talked about Azure, about the amazing growth that Azure is undergoing, and how our colleagues  in the product group have managed this growth.  But I then also talked about the dated tsunami and the problems with the end of Moore's law  and compute networking and storage, and how we with our optical technologies hope to overcome  those.  And I introduced two specific projects in the space of networking and storage, and we  have other projects going on, and looking at how it's going.  Now, we are very hopeful that these technologies one day will run in the data center.  And just to point you to a few upcoming opportunities, and we have a website around our theme of  line of research, opticsforthecloud.com.  So just go there, and colleagues from HPC Group will be in Frankfurt next week.  We also have an increasing presence in the physics and physics conferences and events  with our work, because of course we are very keen to get other physicists and choose to  apply their optics work in the data center.  Okay, thank you very much.  So many thanks Scarlet for this presentation.  Indeed, about beyond the exascale, so you have seen things from femto meters up to set-up  bytes.  So for the sake of time, please, if there's one or two short questions, now would be a  good time to address them to Scarlet.  So thank you for your nice presentation.  You addressed how to address a rack with the color, but what happens if several racks  are talking to the same rack in the same color?  Yeah, that's a good point.  There is actually one part of my presentation that will have been about this, but I had  to cut it out for time reasons.  So this will require very careful scheduling of packets inside the data center.  So a really different approach, because you can't just fire packets off and hope they  arrive, because they might interfere with each other.  So you need a very, you need sort of an overall scheduling approach, and that's why we have  with the time synchronization, why it has been very important for us to get to a nanosecond  scale time synchronization, so that we can make sure that we fire the packets off so  that they don't interfere with each other.  Hello?  Yeah, okay.  So maybe you said already, but how much data can you store on one of these little glasses,  or how much you plan to store?  Yeah, that question always comes up.  So there are some things I can't talk about for competitive reasons, but just one observation.  We are, of course, doing this work in order to land in the data center.  So the rates are competitive.  So if you look at the magnetic and other optics storage media, we have sort of very strict  goals that we need to achieve, and we are on track.  So it's certainly a competitive volume of data that we can store on there, but that's  all I can say.  Okay, so one last short question.  So going back to the optical switching, do you need to have a different wavelength of  light for each destination rack?  And so what happens if you have a thousand different frequencies with a thousand different  racks, and all of a sudden you double your size of your data center?  Do you need to re-encode everything, and all of a sudden you need 2,000 different frequencies?  Yeah, I mean, typically the data center is, of course, you have these small data centers  and regions, et cetera, so there are a certain size, and you'd rather build more data centers  in the region so you can scale it accordingly.  But for the number of racks that are on average there today and in the foreseeable future,  we have enough wavelengths to cover those.  But you would need, yeah, you do need to make sure to have enough wavelengths to get to each  one, but the team is very comfortable that this is no problem.  So cut here, because we have a 15, like less than 15 minutes quick coffee break, which is  kindly sponsored by Microsoft, so we no refresh ourselves for the hard morning we're going  to face.  So thanks for showing up that early.  And so Scarlett said you're around for a little longer, so like more questions can be addressed  to her during the break.  So see you in a bit.  Thank you.
_Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_0_155.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_156_2231.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_2232_4943.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_4944_5975.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_5976_7439.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_7440_7607.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_7608_7667.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_7668_8279.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_8280_8471.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_8472_8879.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_8880_12071.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_12072_12995.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_12996_13043.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_13044_13223.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_13224_13967.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_13968_14315.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_14316_19187.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_19188_20195.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_20196_20543.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_20544_24311.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_24312_24731.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_24732_27347.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_27348_28607.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_28608_29255.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_29256_30671.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_30672_32123.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_32124_32495.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_32496_33203.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_33204_33287.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_33288_34163.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_34164_36143.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_36144_38351.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_38352_39455.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_39456_39755.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_39756_47975.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_47976_49091.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_49092_50651.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_50652_51659.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_51660_52355.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_52356_56951.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_56952_58799.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_58800_60851.png _Microsoft_Sponsored_Keynote__Microsoft_Optics_for_the_Cloud_60852_65919.png
So what I will do in the next 45 minutes is take a research view on data center technologies. And that's important to emphasize. I'm not part of the Microsoft Azure product group, but I'm part of Microsoft research. So there we take a longer term view on technological development, development, so it's three to five years or even longer on the time horizon. So just keep that in mind because what I'm presenting is has nothing to do with product roadmap, although I will talk about Azure as well. And I will indeed start with a quote by our Azure CTO, Marcus Sinevich, that says, the fundamental disruption that the cloud is creating is based on instant access to compute and infinite storage. So instant access to compute and infinite storage, which is of course sort of a great vision statement, especially for the HPC community. But that's sort of the goalpost. That's what ideally we want to achieve with the cloud. But what I will show you in the next 45 minutes is that with current cloud technologies, we are approaching a wall. So why is that? Well, current cloud technologies were conceived before the cloud accesses it and they have evolved a lot and they are still evolving a lot, but they are still hindered by their legacy design. So at MSR in Cambridge, we are taking a new approach. In our research team, we are looking at new technologies for the cloud that exploit optics. And today I will motivate why we use optics and then talk about our work in optical networking and optical storage. But as we start, let's have a look at Microsoft Azure today. So today the infrastructure looks like that. We deploy data centers in more than 50 regions around the world. Each of these data centers has in the order of 10 to 20, each of these regions has in the order of 10 to 20 data centers. And each data center has in the order of 100,000 servers. All this is connected by 10,000s of kilometers of fiber. So it's a vast infrastructure indeed. And keep in mind that has grown enormously. So in the last four to five years, the growth factor has been about 10x. So that is about sort of doubling up every year. And just to give you an example here, so this is a data center that just a few years ago was sort of cutting edge and doing everything to meet customer demands. And this is a wider image where you see the extension taking place and this data center being extended to sort of cover a two mile end to end region. So massive growth really, if you can imagine our colleagues in Microsoft Azure who maintain an infrastructure like this and who basically double up every year. Of course we have an HPC offering. I will not talk about this today, but you're probably aware. I have just been given a couple of slides by our HPC colleagues and there have been some recent announcements, some new HPC offerings that we have in the company that really deliver great performance for HPC workloads. And I have this overview slides where you show the new virtual machines in the HP and HPC series that really for today and for the immediate future, of course, do everything that you need in HPC for HPC workloads. But that's not the topic of today. I will, as I said, take a research view and developments. And there I want to look back and kind of see how does Azure grow? How can it meet this sort of increase in demand? So how do we scale cloud capacity? Well, of course, we have to sort of improve the infrastructure on all levels. That means faster and faster servers, more processing power, more accelerators such as FPGAs and GPUs, and more memory. But we also have to build faster and faster networks. And so in order to meet this, it would 10X and four to five years or doubling up every year growth in the cloud infrastructure. And if this was not enough, there's actually a phenomenon that is summarized here in a slide by Mark Rosinovic that talks about overall data generation. So the amount of data generated by all of mankind is doubling every other year. So let that sink in. We have exponential growth in data, and that's overall data since the start of mankind. So that is, of course, massive. And where does this data come from? Well, you and HPC know more about this. I know an electron microscope, for example, producing terabytes of data every day. But then you have Internet of Things with all this edge data coming in, sensor data coming in from the edge. You have, of course, your consumer data, and you have machine learning and AI workloads driving this further. But how big is a zetabyte? It's kind of quite difficult to get the notion of this. So that's a hard disk, a 10 terabyte capacity, which is sort of cutting edge today. And you would need 100 million of those to get to one zetabyte. So if you stack those up in a sort of tower, that would be 2,500 kilometers high. And if you wanted to transmit that sort of data across a high end data center network link today, that would take you 2,500 years. So our colleagues in Azure are actually talking about the data tsunami. And of course, for them, it's sort of, you know, this is coming and it's sort of working to meet up today and tomorrow and next year. But it's sort of the growth is frightening. So really, with this exponential growth and data, we need, respectively, we need the exponential growth around storage, but of course, also compute and networking. And Microsoft has done a lot to build up the cloud infrastructure accordingly, as I said, by pushing existing technologies to the brink, but also by making considerable investments in custom hardware. So I have a few examples here of custom hardware that Microsoft has built. So for example, as regards compute, we are building our own rec service and contributing these designs to the open compute project in Project Olympus. Regarding storage, we're designing our own custom storage recs for cold storage. So that's a sort of infrequently accessed data in Project Pelican. And just to say that Project Pelican is a project that started almost 10 years ago at the, in Microsoft Research Cambridge, and that was built up to a level of maturity where it could be transferred into Azure and is now running at scale in Azure data centers. So that's a great, it was a great success for the Cambridge team to build up their research to something that can really be productized. And regarding networking in Project Medicine, we're designing our own fiber transmission technology to achieve multi-terrobit speeds between data centers. And finally also regarding networking, we are laying down our own subsea cables between continents in order to provide higher bandwidth and lower latency. So a lot has been achieved, a very vast infrastructure indeed, and that enabled us to sort of grow across the three dimensions. And please keep these in mind because I'll be looking back at compute storage and networking later on. But I also said it's challenging. We have the data tsunami, we're in the middle of this already, of course, and there's more disruption on the horizon. And many of you will kind of know what I'm talking about. It's the end of Moore's law. So the limitations I have with CMOS scaling, they are of course on the horizon as well. And what might be less well known is that I have the end of Moore's law in compute. So there's Moore's law, of course, saying that every other year I get processors that are twice as fast but basically at the flat cost factor. So what's less well known is that I have similar phenomena in storage and networking. There's also a similar phenomena to the end of Moore's law. And we don't know exactly when this will take place, but it's inevitable that it will because of the limitations of CMOS scaling. So all this transistor based technology kind of reaching the limit of what can be achieved. So what we know is that we need new growth curves, right? We need new technologies that give us these exponential growth because we need this because of the dated tsunami. And that of course for an organization like Microsoft Research where we have the luxury to stand back and say three to five years or even longer on the time horizon, what might these technologies be that give us this growth? That's the kind of position we are in at MSR. And so we started a few years ago, probably four, five years ago, looking at the properties that these data centers need to fulfill or these new technologies need to fulfill in order to meet this growth demand. And then looking at different options, evaluating those and we in the Cambridge lab settled on optics. There are other parts of the company where other technologies are investigated. You might have heard about the DNA storage work. So we have a team working on this. We have teams that work on accelerator technology, et cetera. So there is quite a lot going on because of course you can't just go into one direction and hope that it will work out. But in order to overcome this upcoming disruption, you have to of course look at all different possibilities. Right. So that brings me to the topic of the talk, the optics. But let's just briefly summarize. So what I basically said is that Microsoft Azure, the Microsoft cloud is an impressive infrastructure. It has grown massively over the years. But it is facing the two main challenges, the data tsunami, the doubling of data every other year, and the end of Moore's law across compute storage and networking. So we settled on optics and why is that? Well, there have been impressive advances in optical technologies in the 21st century. Just be aware that since 2005, we had four physics Nobel prizes being awarded in the space of optics. So they have been breakthrough innovations in academic labs across the world that are relevant here. And so we started about four, five years ago. We started on sort of individual project collaborations with optics labs around the world. So at the moment, this is just a number of the universities we are working with currently on projects that bring the physics side together with what's needed for the data center to explore sort of different aspects of bringing optical technologies into the cloud. And we found we have formed the optics for the cloud research alliance. So what's so special about this? Well, if you kind of look at this picture, we are now working with in the optics space. So that's really part of physics. But in order to do this work, we need to take a very cross-disciplinary approach. So we need, of course, competence and hardware storage and networking and really have to build very cross-disciplinary teams of people. And that's probably easier in an organization like Microsoft Research than in university because you can just build up the teams depending on funding, of course, but whatever you require. So we have physicists now in-house. We have, of course, computer scientists working on networking, storage technologies, engineers, hardware people, et cetera. So a very cross-disciplinary team of people looking at this from an end-to-end point of view. And we can do this because we own and operate the entire cloud stack. So for example, on the physical level, we develop our own photonic integrated circuits, so called PICS, in the lab and have them fabricated externally. And then later on, I will talk about our storage work and also our work in networking that will be up next. Okay. So let's look at optical networking for the cloud. So in this section, the next 10 to 15 minutes, I will show you that by using light and glass, we can achieve high bandwidth and ultra low latency in the data center. And I will show you how a fast tunable laser that we have designed in the Cambridge lab can fundamentally change the way in which we design networks and applications. So how does data center networking work today? So as mentioned earlier, a typical data center has in the order of 100,000 servers across a few thousand racks. So each rack has so many servers. And on the top of each rack, you have an electrical switch. And this is then connected in a hierarchy of electrical switches so that every rack and the data center can talk to every other rack. So that's what an electrical switch looks like. And it uses so-called pluggable transceivers that take optical signals and convert them into electrical, into the electrical domain, so into zeros and ones. And why is that? Because everything that is transmitted over two meters or more needs to be sent in the optical domain because electrical would not be possible due to signal degradation. So we have to use optics anyway for anything that is two meters or longer. So that means in a hierarchy like this, we need to really, sorry, pointer is this one, yeah. In a hierarchy like this, we need to send the electrical signal, then convert it to optical, then send it the next level up, convert it back to electrical, then back to optical, send it further, et cetera. So what happens is that you convert the package into the electrical domain and then in your switch you have a little microprocessor that does an address lookup, so it kind of needs to find out where the packet has to go next, which hope it has to go next. It then queues the packet at the respective output port. And then my pluggable transceiver converts it back into optical and sends it on. So it's quite complicated because depending on which racks you send your packet to and from, you can have up to five conversions or so between optical, electrical, back and forth. So that is, of course, quite an overhead and that's what's happening in the data center, but that's also the technology that's used in the Internet. And why, I mean, that does look fairly complicated. So why have we settled on this? Why have we been okay? Well because Moore's law for networking is valid still and what this means is basically we get new switch technology every other year that gives us the increase in performance while keeping a flat cost factor. So we kind of more or less get this exponential growth automatically with a technological advance of CMOS technologies. So flat cost factor, which is great because our colleagues in Microsoft Azure don't have to give it that much thought today because they know new switch chips are coming very soon and give them the growth that they need to kind of, to manage the workloads that the customer delivers. Okay, so I mentioned this beforehand, of course. So we have this, the electrical switching exponential growth, but it is tailing off. And this means the tailing off means that we might have new CMOS technologies giving us electrical switches, but they might be more expensive and more expensive with every generation that is coming. So every two, three years when we change the equipment in the data center, we have to pay more, which would give us, gives us an exponential growth in cost factor as well, which of course will not be very popular with the customer. So we instead are looking at optical switching to overcome these limitations. So why optics? Well, we know that we already use optics in the data center with all these conversions for the transmissions across our fibers. So if you kind of think back, we had this fairly complicated hierarchy of electrical switches and the idea would be to kind of try to find some electrical, some optical switch instead. And another reason is that optical technologies are not, are not affected by the limitations of CMOS scaling. So they are most more likely to give us this sort of natural exponential growth with new technology advances. But how can we switch packets using light? I will show you an animation in a couple of minutes. That basically shows that we have two main components that we want to use here. And the idea, it's a very basic, simple idea is that basically for every destination rack that we want to reach, we use a respective wavelength in the optical spectrum. So basically a color. So let's say I want to send a packet to the red rack, then I send it in the red wavelength, green, in the green wavelengths, et cetera. Now do this by using this optical switch that we call, so I start the video now, the so-called arrayed waveguide grating router. So this is a kind of conceptual. And white light is broken into respective wavelengths. And you see red is directed to a specific port, green is directed to a specific port. And that it's basically a passive component. It's grating on glass like a prison technology that uses one respective wavelength for each rack in the data center. And here you see this sort of how we realize it on our photonic integrated circuit. So that's my optical switch, the arrayed waveguide grating router, AWGR. And here you see another component. And that's a tunable laser because of course what we now need is a laser that doesn't give us white light, but that gives us a respective wavelength so that we can encode our packet and the respective color, send it to our optical switch and then have it routed automatically to the receiving rack. So a few years ago we started to look at this tunable laser technology. And we started of course with commodity off the shelf tunable lasers that you can buy at your supplier. So we found that tuning the laser between one wavelength and another took one millisecond. There are people who know about networking know this as a lifetime in networking terms, so absolutely unacceptable. What the team in Cambridge then did is take this sort of commodity off the shelf tunable laser but kind of squeeze every bit of performance out of it that we can by integrating into this board, by designing our own algorithm for tunability, et cetera. Here's one of the prototypes we produced. And we were able to reduce the switching time by four orders of magnitude to 100 nanosecond. Well that sounds great. I mean that's an amazing achievement, right? If you kind of look at this research achievements that have been made. But let's have a look at, you know, a specific example. So let's say we want to send a packet, 256 byte packet to the red rack. And we want to send another packet to the green rack straight afterwards. What would happen now is that basically because of the switching time, I have this delay of 100 nanoseconds in between. I just have to wait before I can send the next packet, right? That means I have a link utilization of less than 30%, which is not acceptable. Again, the overhead is too big. So this didn't work. And that means in MSR Cambridge, we decided to take a fundamentally different approach and design our own photonic integrated circuit, our own PIC. And this is sort of what it's sketched today. You see here, of course, the device that you've seen earlier in the animation. So that's the AWGR, the optical switch. And then on the left-hand side, you basically have lots of little lasers that produce the respective wavelengths. And you can, in principle, go up to hundreds of different wavelengths. So here is the size of the PIC in comparison to a pound coin. And here's basically what happens. So let's assume you're looking back at red and green packets. So the one side of this chip then creates all these different wavelengths. And the other side is basically like a wavelength filter. So what we see here is that when the filter is on green, it only lets through the green wavelengths. If we tune it on red, it only lets through the red wavelengths. And in this way, I can, in principle, use hundreds of different wavelengths to reach every rack in my data center. So this is a device that we designed in the Cambridge lab and that was then fabricated in an external foundry. So now let's look back at our performance, our switching performance, as mentioned before. So how fast can we now switch our tunable laser? Well, it's less than one nanosecond, which is amazing because if we look at our example, then our switching time is almost negligible and I reach almost full link utilization. So, we talked about rack to rack. So let's have a quick look at how that would work with between racks across the whole data center. So basically at the top of each rack, we now have our tunable laser that produces, creates a packet in the respective wavelengths depending on where the packet is going. We don't need plugable transceivers and we don't need this complicated hierarchy of electrical switches. We just convert in the rack our signal into the optical domain and then send it across our passive component about across our AWGR, where it is rooted depending on wavelengths to the destination rack. So now we have the effect that we have optical technology that is not affected by CMOS, by the end of Moore's law, and we can hopefully keep a flat cost factor there. And what we also achieve, and there's of course, I mean, it's a fundamentally new approach to networking. So there is a lot of consequences this has on what's happening today, of course. But if you kind of consider it at the moment, you send packets and you have to buffer them and there is congestion, et cetera. That's of course a big problem in data center networks, where it's now what we are saying is that we send a packet from one rack to another basically at the speed of light. So we know exactly how long the packet will take, in this case 250 nanoseconds, in order to get to the destination rack. And that depends on the length of the distance, on the distance between the racks. So we basically now have a deterministic approach. So we know exactly how long it will take, and that is for applications in HPC, for example, this of course is a great opportunity, because we can rely on this and don't have to plan in arbitrary times of packet processing and buffering. But just to say, so there are, of course, it's a fundamentally new approach. So there are lots of technical challenges. And we have, so we started this project probably even further than that, but about in 2016, we developed this sort of our own version of the board with custom tunable laser. And our own AWGR component that was sort of last year. Then there's another big problem now, because we don't have buffers. We need to synchronize clocks very exactly, but in the different racks of the data center. So we've been able to make impressive advances there last year. And the team is basically now very confident that we can overcome these very difficult problems and one day use optical networks and data center. But just to make clear, this is a very long term view in order to develop such a technology so that it can run at scale and stable and within certain cost factors in the data center. That's of course a huge leap. So that will take years to get there. But yeah, I mean, a few years ago, we didn't kind of think that we would solve these important problems and they have been solved. So there is a lot of optimism with this networking work to go further. Okay, so that was the project introduction to our work in optical networking. And what I will show you next is the work we do in optical storage. And just to say, so with this storage, we're looking at so-called cold or frozen data. So this is archival data that is accessed very infrequently or possibly never. Think about your health insurance data from like 30 years ago. You can't throw it away, but you have to save it somewhere. And the idea here is to develop a storage media from the ground up specifically for the data center. So we were not sort of threw away everything that existed before and really looked from scratch what are the requirements in the data center and how can we meet it. So I brought a little glass sample here. It's basically, it's like three centimeters or so. And what you see there is basically the data is encoded in these little blue dots. And that's encoded inside the bulk of the glass. There are of course other optical storage media like DVDs, Blu-rays, etc. But they are surface technologies. Here my data is encoded inside the bulk of the glass. And this is just the Azure logo. So going back to our dated tsunami, right? Overall data generated growing exponentially. With the dated tsunami, we can, we know that with existing storage technologies, the ratio of data we can afford to store goes down because the existing technologies cannot keep up. I've got a couple of slides of that. I could sort of talk about this at great lengths, but the capacity is not high enough. And the problem is that the lifetime is not high enough either. So this ratio goes down. And what we are trying with Project Silica is to push this curve up so that we can afford to store more data or that we can store data for longer depending on our workloads. So let's look at what we have today. Of course, Azure has offerings across all the different data storage requirements. And you talk about hot data to code to frozen data. So there are current, great running Azure offerings. On the right-hand side, you see the existing technologies that are used. And some people might be surprised to hear that tape is still being used quite extensively in data centers. It's actually quite a good technology. Another big problem is that the spooling takes a long time because these tapes can be like a kilometer long. So our Silica project that sits down there with the code to frozen data with the goal to basically eternally store your data and ideally not have to back it up again. But eternal lifetime of the storage medium. So just very quickly, what are the existing archival storage media? So this is a summary. On the left-hand side, we basically have the magnetic ones, hard disk and magnetic tape. And here the optical ones, optical disks like Blu-ray, DVDs. The problem is with all of these that there are surface technologies. So they suffer from things like it's called bit rot. It's one thing or electromagnetic pulses can affect the data or just electrons leak out. So none of these storage technologies that these existing ones lasts more than 10 years. The longest lifetime is for magnetic tape and that's 10-year lifetime. My hard disk and my optical disk has a lower lifetime. So if you think back at the one zetabyte, if I have the 100 million hard disks, but it takes me three years to copy the data onto my 100 million hard disks, and then at the end of that time, I need another three years to back it up to new storage media, then effectively I might have only two or three years left where the data is actually stored before I have to revisit this. So that doesn't scale very well. So we took inspiration by this and this is basically a clay, a bit of clay, a clay tablet that's in the British Museum. It's 5,000 years old and it's one of the oldest bits of data that we have. And of course it's carving in the clay. So it's a physical change in the material. And that's basically the motivation for us using silica glass. What we want to achieve is that we basically write the data into the storage media once and then leave it there forever. So we want basically thousands of years lifetime. And we don't want these effects that we have with current media. We can't accept it in our technology. And we of course need a cheap material as well. So there's quite a lot of requirements. And what we settled on is basically it's silicon dioxide. It's just basically it's glass. It's like melted sand. So it's a very cheap material. And I'll show you regarding robustness. We tested it. It's one of our researchers doing some tests at home. It's quite an amateur to be here of course. But that's our colleague as a physics scientist, Ariel, who did these tests. And indeed we were able to read the data without a problem afterwards because the data is stored inside the glass. So and you might say why is this possible? Now why was it not possible earlier? Well it's because now we have the femtosecond laser technology. So it's a laser basically that pulses in approximately 100 femtosecond wavelengths. And how much is a femtosecond? 10 to the power of minus 15 seconds. So very, very, very short. So the idea with this femtosecond laser is that you basically direct the laser beam through a lens inside the bulk of the silica glass. And what happens then is that a voxel is created. That's a three-dimensional little structure. And if we kind of look back to the time before the femtosecond laser, we had the picosecond laser, and what you see here on a larger scale of 50 nanometers, you see that cracks form in the glass. So basically it's sort of the wavelength is not short enough. It's not a you can't read any data out of this. But with the femtosecond laser, and now we are looking at 10 nanometers, we get really very well-defined voxels in our glass. And you saw earlier, I mean, there were just a number of probably 10 of these blocks of arrays of dots in this glass, which is not putting it to maximum capacity, but it's all very, very dense. And this has enabled us to design this technology and project silica. So how do you store the data in glass? How are these voxels formed? So basically what happens is that depending on the pulse and the polarization of your laser beam, the voxels have a certain volume, a size, x, y, yz direction, the volume, but they also have these sort of nano gratings inside the glass. So we're working with this with the University of Southampton, and there have been relevant findings in the early 2003 that basically you get these nano gratings within the voxel. So basically you can say it's the 3D position of the voxel inside the glass, and then the size of the voxel, and the orientation of the nano gratings, where we encode the bits, basically. Okay, in the one point I want to make, I will finish with an animation, but just one point to make, the good thing with this storage technology is that we can have the right store and read completely disaggregated. That means I have my right system somewhere and write my data in glass, then store it in a glass library, and then move it to the read system as and when I need it. For example, with hard disk, you basically get the read and write heads as part of the package. So here I can design it depending on how much read I need for my data. Okay, and I'll skip over those. There are just some schematic things about the write and the read process, but they're basically summarized in this animation that we've done that shows here the write process with my femtosecond laser burning these voxels, or not burning it sort of the stage beforehand, very small voxels into the bulk of the glass. And in this way, we can form a 3D, three-dimensional array of voxels inside the glass, and have lots of these little arrays and one glass, little glass square. And then once it's written, we put it in our glass library, and then later on read it out with some high definition cameras, and basically what happens is we just take an image layer by layer in these arrays, and go through top to bottom, take a picture of each of these layers, and then use computer vision techniques to read out the data. Okay, so that brings me to the end of my talk to quickly summarize what I did. I talked about Azure, about the amazing growth that Azure is undergoing, and how our colleagues in the product group have managed this growth. But I then also talked about the dated tsunami and the problems with the end of Moore's law and compute networking and storage, and how we with our optical technologies hope to overcome those. And I introduced two specific projects in the space of networking and storage, and we have other projects going on, and looking at how it's going. Now, we are very hopeful that these technologies one day will run in the data center. And just to point you to a few upcoming opportunities, and we have a website around our theme of line of research, opticsforthecloud.com. So just go there, and colleagues from HPC Group will be in Frankfurt next week. We also have an increasing presence in the physics and physics conferences and events with our work, because of course we are very keen to get other physicists and choose to apply their optics work in the data center. Okay, thank you very much. So many thanks Scarlet for this presentation. Indeed, about beyond the exascale, so you have seen things from femto meters up to set-up bytes. So for the sake of time, please, if there's one or two short questions, now would be a good time to address them to Scarlet. So thank you for your nice presentation. You addressed how to address a rack with the color, but what happens if several racks are talking to the same rack in the same color? Yeah, that's a good point. There is actually one part of my presentation that will have been about this, but I had to cut it out for time reasons. So this will require very careful scheduling of packets inside the data center. So a really different approach, because you can't just fire packets off and hope they arrive, because they might interfere with each other. So you need a very, you need sort of an overall scheduling approach, and that's why we have with the time synchronization, why it has been very important for us to get to a nanosecond scale time synchronization, so that we can make sure that we fire the packets off so that they don't interfere with each other. Hello? Yeah, okay. So maybe you said already, but how much data can you store on one of these little glasses, or how much you plan to store? Yeah, that question always comes up. So there are some things I can't talk about for competitive reasons, but just one observation. We are, of course, doing this work in order to land in the data center. So the rates are competitive. So if you look at the magnetic and other optics storage media, we have sort of very strict goals that we need to achieve, and we are on track. So it's certainly a competitive volume of data that we can store on there, but that's all I can say. Okay, so one last short question. So going back to the optical switching, do you need to have a different wavelength of light for each destination rack? And so what happens if you have a thousand different frequencies with a thousand different racks, and all of a sudden you double your size of your data center? Do you need to re-encode everything, and all of a sudden you need 2,000 different frequencies? Yeah, I mean, typically the data center is, of course, you have these small data centers and regions, et cetera, so there are a certain size, and you'd rather build more data centers in the region so you can scale it accordingly. But for the number of racks that are on average there today and in the foreseeable future, we have enough wavelengths to cover those. But you would need, yeah, you do need to make sure to have enough wavelengths to get to each one, but the team is very comfortable that this is no problem. So cut here, because we have a 15, like less than 15 minutes quick coffee break, which is kindly sponsored by Microsoft, so we no refresh ourselves for the hard morning we're going to face. So thanks for showing up that early. And so Scarlett said you're around for a little longer, so like more questions can be addressed to her during the break. So see you in a bit. Thank you.
conceived before the cloud accesses it and they performance for HPC workloads. And I have this will, as I said, take a research view and infrastructure. And if this was not enough, provide higher bandwidth and lower latency. So a across the three dimensions. And please keep We have teams that work on accelerator hardware storage and networking and really have networking, storage technologies, engineers, data center networking work today? So as limitations of CMOS scaling. So they are most technology that is not affected by CMOS, by the introduction to our work in optical networking. requirements in the data center and how can we of the oldest bits of data that we have. And of law and compute networking and storage, and how