39326914
Parameterized subscriptions in publish/subscribe systems
Traditional publish/subscribe systems commonly deal with static subscriptions, whose event filtering criteria stay fixed once defined. Although systems with static subscriptions are simpler to implement, there are cases where the subscription criterion involves state that changes frequently over time. Rather than having the user re-submit his/her subscription repeatedly, we propose parameterized subscriptions as a systematic solution for adaptive subscriptions. Parameterized subscriptions depend on one or more parameters, which are state variables stored and maintained automatically by the publish/subscribe servers. In this paper, we modify traditional publish/subscribe protocols in order to deal with parameterized subscriptions. We then look at certain optimizations that improve the efficiency by controlling where and how much state is allocated in the system. Finally, we present a simple evaluation framework to illustrate the fundamental operating differences between several schemes.
3017976
Publish/Subscribe Tree Construction in Wireless Ad-Hoc Networks
Wireless ad-hoc publish/subscribe systems combine a publish/subscribe mechanism with wireless ad-hoc networking. The combination, although very attractive, has not been studied extensively in the literature. This paper addresses an important problem of such systems: how to construct an optimal publish/ subscribe tree for routing information from the source to all interested recipients. First we precisely define the optimality of a publish/subscribe tree by developing a metric to evaluate its "efficiency." The optimality metric takes into account both the goal of a publish/subscribe system (i.e., to route a set of events), and the characteristics of an ad-hoc network (for example, devices are resource limited). We propose a greedy algorithm, SHOPPARENT, which builds the publish/ subscribe tree in a fully distributed fashion. A key feature is that this algorithm can be "subscription-aware", allowing it to use publication/subscription information in order to find a better outcome. Our simulations show that SHOPPARENT's performance is within 15% of optimal under normal configurations. We also study the effect of geographically localized subscriptions.
14183146
Replicated condition monitoring
A condition monitoring system tracks real-world variables and alerts users when a predefined condition becomes true, e.g., when stock price drops, or when a nuclear reactor over-heats. Replication of monitoring servers can reduce the probability that an important alert is missed. However, replicated independent servers can sometimes report “conflicting” alerts to the user, causing confusion. In this paper, we study the problem of replicated condition monitoring. We identify and formally define three desirable properties of a replicated system, namely, orderedness, consistency, and completeness. We propose new monitoring algorithms that enforce some or all of the desired properties in different scenarios.
53245927
Cellular disco: resource management using virtual clusters on shared-memory multiprocessors
Despite the fact that large-scale shared-memory multiprocessors have been commercially available for several years, system software that fully utilizes all their features is still not available, mostly due to the complexity and cost of making the required changes to the operating system. A recently proposed approach, called Disco, substantially reduces this development cost by using a virtual machine monitor that laverages the existing operating system technology. In this paper we present a system called Cellular Disco that extends the Disco work to provide all the advantages of the hardware partitioning and scalable operating system approaches. We argue that Cellular Disco can achieve these benefits at only a small fraction of the development cost of modifying the operating system. Cellular Disco effectively turns a large-scale shared-memory multiprocessor into a virtual cluster that supports fault containment and heterogeneity, while avoiding operating system scalability bottlenecks. Yet at the same time, Cellular Disco preserves the benefits of a shared-memory multiprocessor by implementing dynamic, fine-grained resource sharing, and by allowing users to overcommit resources such as processors and memory. This hybrid approach requires a scalable resource manager that makes local decisions with limited information while still providing good global performance and fault containment. In this paper we describe our experience with a Cellular Disco prototype on a 32-processor SGI Origin 2000 system. We show that the execution time penalty for this approach is low, typically within 10% of the best available commercial operating system formost workloads, and that it can manage the CPU and memory resources of the machine significantly better than the hardware partitioning approach.
26991946
Publish/Subscribe in a Mobile Environment
A publish/subscribe system dynamically routes and delivers events from sources to interested users, and is an extremely useful communication service when it is not clear in advance who needs what information. In this paper we discuss how a publish/subscribe system can be extended to operate in a mobile environment, where events can be generated by moving sensors or users, and subscribers can request delivery at handheld and/or mobile devices. We describe how the publish/subscribe system itself can be distributed across multiple (possibly mobile) computers to distribute load, and how the system can be replicated to cope with failures, message loss, and disconnections.
15965432
Efficient key distribution schemes for secure media delivery in pay-TV systems
To provide secure media delivery in pay-TV systems, a large number of messages are exchanged for key updates in the conventional key distribution schemes. This is inefficient and costly when the client side (set-top box) uses a smart card with limited computing power. In this paper, we present three key distribution schemes for channel protection and secure media delivery in pay-TV systems. With the proposed schemes, encryption keys of the subscribed programs can be efficiently and securely distributed to the authorized subscribers. Only one message is needed to renew key in the key distribution schemes for subscription channel protection. In addition, we use simpler computation functions, including one-way hash function and exclusive-OR operation, for key updates to reduce the computation cost. With our key distribution schemes, only authorized subscribers can watch the subscribed programs correctly. Unauthorized subscribers have no information to retrieve the correct programs over the networks. Thus, service providers can charge their subscribers according to their subscriptions, and the illegal access of the media and video programs from networks can be prevented, based on the proposed schemes.
10038411
On personal data license design and negotiation
Web applications often require users to provide personal information for customization, identification, or completing a transaction. However, the way we disclose our information in current cyberspace does not allow us to control the usage, release and circulation of the information, violating the basic principle of personal information privacy. Online personal data licensing (OPDL) is a framework to bring the control of personal information back to individuals by requiring application and service providers to obtain a license from a person before collecting, processing, and using his personal information. In this paper we present the license design and automation of the license negotiation process.
5254746
Low-power techniques for network security processors
In this paper, we present several techniques for low-power design, including a descriptor-based low-power scheduling algorithm, design of dynamic voltage generator, and dual threshold voltage assignments, for network security processors. The experiments show that the proposed methods and designs provide the opportunity for network security processors to achieve the goals of both high performance and low power.
15221020
Learning Declarative Control Rules for Constraint-BAsed Planning
Despite the long history of research in using machine learning to speed-up state-space planning, the techniques that have been developed are not yet in widespread use in practical planning systems. One limiting factor is that traditional domain-independent planning systems scale so poorly that extensive learned control knowledge is required to raise their performance to an acceptable level. Therefore, work in this area has focused on learning large numbers of control rules that are specific to the details of the underlying planning algorithms, which can be extremely costly. In recent years, a new generation of planning systems with much improved speed and scalability has become available. These systems formulate planning as solving a large constraint satisfaction problem. This formulation opens up the possibility that domain-specific control knowledge can be added to the planner in a purely declarative manner via a set of additional constraints. In this paper we present the first positive results on automatically acquiring such high-level, declarative constraints using machine learning techniques. In particular, we will show that a new heuristic method for generating training examples together with a rule induction algorithm can learn useful control rules in a variety of domains. Only a small number of rules are needed to reduce solution times by two orders of magnitude or more on larger problems, training times are short, and the learned rules can be exported to other planning systems.
764405
Quantization based on statistical moments for signal detection: design and analysis
This paper addresses a signal detection problem based on a class of quantizers. The known moments of the underlying noise distribution are used to index a set of quantization points that have been predetermined under an assumed noise model. The fact that lower order moments are easy to obtain and that they are required in the implementation and analysis of most threshold detectors makes this approach quite appealing. The performance of the resulting quantizers is shown to be relatively insensitive to variations in the underlying noise distribution and to small deviations of the presumed moments. The detector's performance in the finite-sample-size case is investigated, and the distribution resulting in the highest false alarm rate is described for both symmetric and asymmetric noise cases. By setting the test threshold according to the worst distribution, a lower bound on the detector's performance is guaranteed. >