An Evolutionary Technique for Performance-Energy-Temperature Optimized Scheduling of Parallel Tasks on Multi-Core Processors

This paper proposes a static multi-objective evolutionary algorithm (MOEA)-based task scheduling approach for determining Pareto optimal solutions with simultaneous optimization of performance (P), energy (E), and temperature (T). Our algorithm includes problem-specific techniques for solution encoding, determining the initial population of the solution space, and the genetic operators that collectively work on generating efficient solutions in fast turnaround time. Multiple schedules offer a diverse range of values for makespan, total energy consumed, and peak temperature and thus present an efficient way of identifying trade-offs among the desired objectives, for a given application and architecture pair. We also propose a methodology to select one solution from the Pareto front given the user’s preference. The proposed algorithm for solving the task to core scheduling effectively achieves 3-way optimization and does so with fast turnaround time. We show that the proposed algorithm is advantageous because it reduces both energy and temperature together rather than in isolation. The proposed algorithm is evaluated using both implementation and simulation and is compared with integer linear programming solutions as well as with other scheduling algorithms that are energy- or thermal-aware. The time complexity of the proposed scheme is also considerably better than the compared algorithms.