Using game theory for scheduling tasks on multi-core processors for simultaneous optimization of performance and energy

Multi-core processors are beginning to revolutionize the landscape of high-performance computing. In this paper, we address the problem of power-aware scheduling/mapping of tasks onto heterogeneous and homogeneous multi-core processor architectures. The objective of scheduling is to minimize the energy consumption as well as the makespan of computationally intensive problems. The multi-objective optimization problem is not properly handled by conventional approaches that try to maximize a single objective. Our proposed solution is based on game theory. We formulate the problem as a cooperate game. Although we can guarantee the existence of a Bargaining Point in this problem, the classical cooperative game theoretical techniques such as the Nash axiomatic technique cannot be used to identify the Bargaining Point due to low convergence rates and high complexity. Hence, we transform the problem to a max-max-min problem such that it can generate solutions with fast turnaround time.