Achieving utility arbitrarily close to the optimal with limited energy

Energy is one of the limited resources for modern systems, especially battery-operated devices and personal digital assistants. The backlog in new technologies for a 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 applications' requirement. We consider the following problem: a system achieves a certain amount of utility from a set of applications by providing certain levels of quality of service (QoS). We want to allocate the limited system resources to get the maximal system utility. We formulate this utility maximization problem, which is NP-hard in general, 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 allocation of resources to actually achieve such solutions. Simulation 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.