Electrical & Computer Engineering Research Works

Permanent URI for this collectionhttp://hdl.handle.net/1903/1658

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    Dual-Processor Design of Energy Efficient Fault-Tolerant System
    (IEEE, 2006-09) Hua, Shaoxiong; Pari, Pushkin R.; Qu, Gang
    A popular approach to guarantee fault tolerance in safety-critical applications is to run the application on two processors. A checkpoint is inserted at the comple- tion of the primary copy. If there is no fault, the sec- ondary processor terminates its execution. Otherwise, should the fault occur, the second processor continues and completes the application before its deadline. In this paper, we study the energy efficiency of such dual- processor system. Specifically, we first derive an opti- mal static voltage scaling policy for single periodic task. We then extend it to multiple periodic tasks based on worst case execution time (WCET) analysis. Finally, we discuss how to further reduce system’s energy con- sumption at run time by taking advantage of the actual execution time which is less than the WCET. Simula- tion on real-life benchmark applications shows that our technique can save up to 80% energy while still provid- ing fault tolerance.
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    Achieving Utility Arbitrarily Close to the Optimal with Limited Energy
    (IEEE, 2000-07) Qu, Gang; Potkonjak, Miodrag
    Energy is one of the limited resources for modern systems, especially the battery-operated devices and personal digi- tal assistants. The backlog in new technologies for more powerful battery is changing the traditional system design philosophies. For example, due to the limitation on battery life, it is more realistic to design for the optimal benefit from limited resource rather than design to meet all the applica- tions' requirement. We consider the following problem: a system achieves a certain amount of utility from a set of applications by providing them certain levels of quality of service (QoS). We want to allocate the limited system re- sources to get the maximal system utility. We formulate this utility maximization problem, which is NP-hard in gen- eral, and propose heuristic algorithms that are capable of finding solutions provably arbitrarily close to the optimal. We have also derived explicit formulae to guide the alloca- tion of resources to actually achieve such solutions. Simu- lation shows that our approach can use 99.9% of the given resource to achieve 25.6% and 32.17% more system utilities over two other heuristics, while providing QoS guarantees to the application program.
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    Energy Minimization with Guaranteed Quality of Service
    (IEEE, 2000-07) Qu, Gang; Potkonjak, Potkonjak; Copyright © 2000 IEEE. Reprinted from ACM/IEEE International Symposium on Low Power Electronics and Dsign. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Maryland's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
    Quality of service (QoS) is one of the key features for new Internet-based multimedia and other applications. Mean- while, energy remains as a big concern for systems that per- form such applications. We address the issue of combining system design concerns and QoS requirements to design systems that can deliver QoS guarantees. In this paper, we discuss how to satisfy QoS requirements and minimize the system's energy consumption. Specifically, we consider the following problem: Given a set of applications each specifying its required amount of computation and service time, how we allocate CPU time and determine the voltage profile on a variable voltage system, such that all the applications' requirements are satisfied and the system's total energy con- sumption is minimized. We optimally solve several basic cases and propose a dynamic programming procedure for the general case. Simulation shows that the new approach saves 38.75% energy over the system shut-down technique.