Fuzzy multi-objective energy optimization of workflow scheduling

Abstract

Task scheduling is a key and challenging problem in cloud computing systems, requiring decisions regarding resource allocation to tasks to optimize a perfor mance criterion. This problem has required researchers and developers to over come significant challenges. Our goal in this study aims to minimize both the makespan and energy consumption in cloud computing systems by efficiently scheduling workflows. To achieve this, we first proposed a dynamic multi objective model, which wasthensimplified into a single-objective problem using dynamic weights. Then, we proposed a dynamic genetic algorithm (DGA) and a dynamic particle swarm optimization algorithm (DPSO) to address the prob lem. To deal with the situation where the makespan is uncertain and not exact, we present a fuzzy model, treating each value as a fuzzy number and we utilize both possibility and necessity metrics. The results are contrasted with the Het erogeneous earliest finish time (HEFT) algorithm and Considerably lowered the total energy consumption, especially for DGA.