Achieving Utility Arbitrarily Close to the Optimal with Limited Energy

Abstract

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.