Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Server farms today consume more than 1.5% of the total electricity in the U.S. at a cost of nearly $4.5 billion. Given the rising cost of energy, many industries are now seeking solutions for how to best make use of their available por. An important question which arises in this context is how to distribute available por among servers in a server farm so as to get maximum performance.   By giving more por to a server, one can get higher server frequency (speed). Hence it is commonly believed that, for a given por budget, performance can be maximized by operating servers at their highest por levels. Hover, it is also conceivable that one might prefer to run servers at their lost por levels, which allows more servers to be turned on for a given por budget. To fully understand the effect of por allocation on performance in a server farm with a fixed por budget,  introduce a queueing theoretic model, which allows us to predict the optimal por allocation in a variety of scenarios. Results are verified via extensive experiments on an IBM BladeCenter.    find that the optimal por allocation varies for different scenarios. In particular, it is not always optimal to run servers at their maximum por levels. There are scenarios where it might be optimal to run servers at their lost por levels or at some intermediate por levels. Our analysis shows that the optimal por allocation is non-obvious and depends on many factors such as the por-to-frequency relationship in the processors, the arrival rate of jobs, the maximum server frequency, the lost attainable server frequency and the server farm configuration. Furthermore, our theoretical model allows us to explore more general settings than  can implement, including arbitrarily large server farms and different por-to-frequency curves. Importantly,  show that the optimal por allocation can significantly improve server farm performance, by a factor of typically 1.4 and as much as a factor of 5 in some cases.
['Anshul Gandhi', 'Mor Harchol-Balter', 'Rajarshi Das', 'Charles R. Lefurgy']
I am writing a research paper on a idea Optimal power allocation in server farms
[{"from": "human", "value": "I am writing a research paper on a idea Optimal power allocation in server farms"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Server farms today consume more than 1.5% of the total electricity in the U.S. at a cost of nearly $4.5 billion. Given the rising cost of energy, many industries are now seeking solutions for how to best make use of their available por. An important question which arises in this context is how to distribute available por among servers in a server farm so as to get maximum performance.   By giving more por to a server, one can get higher server frequency (speed). Hence it is commonly believed that, for a given por budget, performance can be maximized by operating servers at their highest por levels. Hover, it is also conceivable that one might prefer to run servers at their lost por levels, which allows more servers to be turned on for a given por budget. To fully understand the effect of por allocation on performance in a server farm with a fixed por budget,  introduce a queueing theoretic model, which allows us to predict the optimal por allocation in a variety of scenarios. Results are verified via extensive experiments on an IBM BladeCenter.    find that the optimal por allocation varies for different scenarios. In particular, it is not always optimal to run servers at their maximum por levels. There are scenarios where it might be optimal to run servers at their lost por levels or at some intermediate por levels. Our analysis shows that the optimal por allocation is non-obvious and depends on many factors such as the por-to-frequency relationship in the processors, the arrival rate of jobs, the maximum server frequency, the lost attainable server frequency and the server farm configuration. Furthermore, our theoretical model allows us to explore more general settings than  can implement, including arbitrarily large server farms and different por-to-frequency curves. Importantly,  show that the optimal por allocation can significantly improve server farm performance, by a factor of typically 1.4 and as much as a factor of 5 in some cases."}]