A slave ants based ant colony optimization algorithm for task scheduling in cloud computing environments

作者：Young-Ju Moon, Heonchang Yu, Joon-Min Gil, JongBeom Lim · 发表于：Human-centric Computing and Information Sciences · 年份：2017 · DOI：10.1186/s13673-017-0109-2 · 被引用次数：89 · 研究领域：Cloud Computing and Resource Management、IoT and Edge/Fog Computing、Distributed and Parallel Computing Systems

Abstract Since cloud computing provides computing resources on a pay per use basis, a task scheduling algorithm directly affects the cost for users. In this paper, we propose a novel cloud task scheduling algorithm based on ant colony optimization that allocates tasks of cloud users to virtual machines in cloud computing environments in an efficient manner. To enhance the performance of the task scheduler in cloud computing environments with ant colony optimization, we adapt diversification and reinforcement strategies with slave ants. The proposed algorithm solves the global optimization problem with slave ants by avoiding long paths whose pheromones are wrongly accumulated by leading ants.