考虑负载平衡的科学工作流容错聚类算法研究  

Research on Fault Tolerant ClusteringAlgorithmof ScientificWorkflow Considering Load Balancing

作　　者：高玮军[1] 张春霞 杨杰 师阳 GAO Weijun;ZHANG Chunxia;YANG Jie;SHI Yang(School of Computer and Communication,Lanzhou University of Technology,Lanzhou 730050,China)

机构地区：[1]兰州理工大学计算机与通信学院,兰州730050

出　　处：《计算机工程与应用》2020年第23期53-60,共8页Computer Engineering and Applications

基　　金：国家自然科学基金(No.6172059);甘肃省引导创新发展项目(No.062004)。

摘　　要：科学工作流执行过程中,多个任务组成的聚类作业相对单任务故障风险更高。容错聚类算法在进行故障恢复的同时面临着负载不平衡问题,为此提出了一种平衡重聚类算法(Balanced Re-clustering,BR)。该算法结合水平运行时间平衡聚类算法(Horizontal Runtime Balancing,HRB)对选择重聚类方法(Selective Re-clustering,SR)进行改进,将运行时间最长的任务分配给运行时间最短的类,在故障发生后重新运行失败的任务。实验结果表明,与现有的两种任务重聚类方法相比,BR算法的性能增益最高分别可达84%和18.75%,显著降低了工作流执行成本,提高了系统的运行效率。In the process of scientific workflow execution,a cluster job composed of multiple tasks has a higher risk of failure than a single task.The fault-tolerant clustering algorithm is faced with load imbalance problems during fault recovery.A Balanced Re-clustering(BR)algorithm is proposed for this purpose.This algorithm combines Horizontal Runtime Balancing(HRB)and Selective Re-clustering(SR)to assign the longest running task to the shortest running class,after re-running the failed task.The experimental results show that compared with the two existing task re-clustering methods,the performance gain of the BR algorithm is up to 84%and 18.75%,respectively,which significantly reduces the workflow execution cost and improves the system’s operating efficiency.

关 键 词：任务聚类 科学工作流 系统开销 容错算法 平衡聚类 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]