Chemical Reaction Multi-Objective Optimization for Cloud Task DAG Scheduling

Abstract

Cloud computing systems often have two conflicting objective, maximizing service performance, and minimizing computing cost. The excellent task scheduling and resource allocation strategies can improve the cost/utility ratio efficiently. It is an NP-hard problem to optimize task scheduling of precedence-constrained parallel tasks represented by a directed acyclic graph (DAG) on the cloud system. In order to address this problem, a chemical reaction multi-objective optimization algorithm (CRMO) is proposed in this paper. The CRMO executes four chemical reaction operators (named on-wall ineffective collision, inter-molecular ineffective collision, decomposition, and synthesis) for cloud tasks DAG scheduling. The experimental results show that CRMO can produce outstanding cloud task scheduling solutions set.