计算机工程与科学2018，Vol.40Issue(5)：765-772,8.DOI:10.3969/j.issn.1007-130X.2018.05.001
一种云环境下基于混合型BBO的任务调度算法
A hybrid biogeography-based optimization algorithm for task scheduling in cloud computing
摘要
Abstract
Task scheduling plays a critical role in cloud computing and is a key factor affecting the performance of cloud computing.It has been proved to be an NP problem.Heuristic algorithm is one of the most effective methods to solve this problem.This paper focuses on the Biogeography-Based Optimization (BBO) algorithm,which serves in recent years as a new heuristic algorithm.Because the BBO algorithm converges slowly in the solution process,by combining Particle Swarm Optimization (PSO) algorithm,we propose a novel task scheduling algorithm,named Hybrid Migrating Biogeography-Based Optimization (HMBBO).A comparison experiment using Makespan as the objective function is performed on the Cloudsim cloud simulation platform.The experiment results show that,compared with several classical heuristic algorithms,HMBBO has the advantages of strong optimization ability,fast convergence speed and high-quality solution,and provides a new way to solving the task scheduling problem in cloud computing environment.关键词
云计算/任务调度/BBO/MakespanKey words
cloud computing/task scheduling/BBO/Makespan分类
信息技术与安全科学引用本文复制引用
童钊,陈洪剑,陈明,梅晶,刘宏..一种云环境下基于混合型BBO的任务调度算法[J].计算机工程与科学,2018,40(5):765-772,8.基金项目
国家自然科学基金(61502165) （61502165）
湖南省教育厅一般项目(17C0959) （17C0959）
