A delay-based dynamic scheduling algorithm for bag-of-task workflows with stochastic task execution times in clouds

被引:59
作者
Cai, Zhicheng [1 ,4 ,5 ]
Li, Xiaoping [2 ]
Ruiz, Ruben [3 ]
Li, Qianmu [1 ]
机构
[1] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing, Jiangsu, Peoples R China
[2] Southeast Univ, Sch Comp Sci & Engn, Nanjing, Jiangsu, Peoples R China
[3] Acc B Univ Politecn Valencia, Inst Tecnol Informat, Valencia, Spain
[4] Key Lab Image & Video Understanding Social Safety, Nanjing, Peoples R China
[5] Southeast Univ, Minist Educ, Key Lab Comp Network & Informat Integrat, Nanjing, Jiangsu, Peoples R China
来源
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE | 2017年 / 71卷
基金
中国国家自然科学基金;
关键词
Cloud computing; Scheduling; Workflow; Bag of tasks; Stochastic; RESOURCE; PREDICTION; NETWORKS;
D O I
10.1016/j.future.2017.01.020
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Bag-of-Tasks (BoT) workflows are widespread in many big data analysis fields. However, there are very few cloud resource provisioning and scheduling algorithms tailored for BoT workflows. Furthermore, existing algorithms fail to consider the stochastic task execution times of BoT workflows which leads to deadline violations and increased resource renting costs. In this paper, we propose a dynamic cloud resource provisioning and scheduling algorithm which aims to fulfill the workflow deadline by using the sum of task execution time expectation and standard deviation to estimate real task execution times. A bag-based delay scheduling strategy and a single-type based virtual machine interval renting method are presented to decrease the resource renting cost. The proposed algorithm is evaluated using a cloud simulator ElasticSim which is extended from CloudSim. The results show that the dynamic algorithm decreases the resource renting cost while guaranteeing the workflow deadline compared to the existing algorithms. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:57 / 72
页数:16
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