Performance, Energy, and Temperature Enabled Task Scheduling using Evolutionary Techniques

In allocating parallel tasks to cores, most energy and thermal-aware scheduling techniques rely on Dynamic Voltage and Frequency Scaling (DVFS) to mark up and down core speeds for running the system under the desired constraints. While these techniques often meet the imposed system constraints, they are maladroit at identifying the best possible trade-offs between performance and energy, or between performance and temperature. This paper focuses on task-to-core allocation for optimizing performance (P), energy (E), and temperature (T) simultaneously. A solution set yielded by such algorithm comprises of multiple points forming a Pareto-front, not just scalar values. This paper employs Strength Pareto Evolutionary Algorithm (SPEA) and Non-Dominated Sorting Genetic Algorithm (NSGA), which have been demonstrated to be superior evolutionary optimization approaches in several domains. The paper utilizes and compares these techniques in DVFS-based PET-enabled scheduling algorithms, and highlights the differences between the two approaches. The paper also explores how the algorithmic characteristics affect the performance of the scheduling schemes. A variety of criteria combined with extensive experimentation help to compare the two approaches. The results show how varying different system and task parameters affect not just the PET goals individually and collectively but also the quality of trade-offs as well as the spread of solutions on the Pareto-front.