Simultaneous optimization of performance, energy and temperature for DAG scheduling in multi-core processors

This paper addresses the joint optimization of performance, energy, and temperature, termed as PET - optimization. This multi-objective PET-optimization is achieved in scheduling DAGs on multi-core systems. Our technique is based on multi-objective evolutionary algorithm (MOEA) for finding Pareto optimal solutions using scheduling and voltage selection. These solutions are not necessarily scalar values but can be in a vector form. We developed a Strength Pareto Evolutionary Algorithm [2] (SPEA) based solution which is inherently superior to several other MOEA methods. The proposed algorithm obtains the Pareto vectors (or fronts) efficiently. The work is novel and original in the sense that no previous such optimization work has been reported to our knowledge for the PET-optimization scheduling problem. The strength of the proposed algorithm is that it achieves diverse range of energy and thermal improvements while staying close to the performance-optimal point to ensure efficient trade-off solutions. The proposed approach consists of two-steps. In the first step, Pareto fronts are generated. In the second step, one most optimal solution is selected. Simulation results on several benchmark task graph applications demonstrate that efficient solutions can be selected using the proposed selection method in polynomial time.