混合云环境下考虑工作流的任务调度策略  

Task scheduling strategy for workflow in hybrid cloud environment

作　　者：翟青海 谢晓兰[1,2] ZHAI Qing-hai;XIE Xiao-lan(College of Information Science and Engineering,Guilin University of Technology,Guilin 541006,China;Guangxi Key Laboratory of Embedded Technology and Intelligent System,Guilin University of Technology,Guilin 541006,China)

机构地区：[1]桂林理工大学信息科学与工程学院,广西桂林541006 [2]桂林理工大学广西嵌入式技术与智能系统重点实验室,广西桂林541006

出　　处：《桂林理工大学学报》2021年第4期891-896,共6页Journal of Guilin University of Technology

基　　金：国家自然科学基金项目(61762031);广西科技重大专项(AA19046004);广西自然科学基金项目(2021JJA170130)。

摘　　要：针对混合云环境下的工作流调度问题,提出一种结合黏菌算法和粒子群算法思想的工作流调度算法。利用粒子群算法的优点,对较差解进行引导,加快较差解的收敛速度,对质量较好的解进行局部变异,避免陷入局部最优,同时增加交叉算子,细化算法在任务调度问题中的寻优粒度,根据混合云环境下的工作流调度模型,进行离散编码,完成对多个调度目标的优化。在现实工作流下进行仿真实验,结果表明,本文算法与单一启发式算法以及对应改进算法相比,具有更好的优化性能。For workflow scheduling in hybrid cloud environment,a new workflow scheduling algorithm combining slime mold algorithm and particle swarm optimization algorithm was proposed.The advantages of particle swarm optimization can guide the poor solution and accelerate the convergence of the poor solution.The solution with good quality is locally mutated to avoid falling into local optimum.At the same time,the crossover operator is added to refine the search strength of the algorithm in the task scheduling problem.According to the workflow scheduling model in hybrid cloud environment,discrete coding is carried out to complete the optimization of multiple scheduling objectives.The simulation results show that the proposed algorithm has better optimization performance than the single heuristic algorithm and the other improved algorithm.

关 键 词：云计算 混合云 任务调度 工作流 黏菌算法 粒子群算法 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]