ElasticSim: A Toolkit for Simulating Workflows with Cloud Resource Runtime Auto-Scaling and Stochastic Task Execution Times

被引:25
作者
Cai, Zhicheng [1 ,2 ]
Li, Qianmu [1 ]
Li, Xiaoping [3 ]
机构
[1] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing, Jiangsu, Peoples R China
[2] Southeast Univ, Minist Educ, Key Lab Comp Network & Informat Integrat, Nanjing, Jiangsu, Peoples R China
[3] Southeast Univ, Sch Engn & Comp Sci, Nanjing, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
Simulator; Cloud computing; Workflow scheduling; Stochastic execution time; Auto-scaling; PUBLIC CLOUDS; INFRASTRUCTURE; PREDICTION;
D O I
10.1007/s10723-016-9390-y
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Resource provisioning and scheduling are crucial for cloud workflow applications. Simulation is one of the most promising evaluation methods for different resource provisioning and scheduling algorithms. However, existing simulators for Cloud workflow applications fail to provide support for resource runtime auto-scaling and stochastic task execution time modeling. In this paper, a workflow simulator ElasticSim is introduced, which is an extension of the popular used CloudSim simulator by adding support for resource runtime auto-scaling and stochastic task execution time modeling. Most of existing workflow scheduling algorithms are static and are based on deterministic task execution times. By the aid of ElasticSim, the practical performance of existing static algorithms, when they are put into practice with stochastic task execution times, is evaluated. Experimental results show that about 2.8 % to 20 % additional resource rental cost is incurred for different cases and workflow deadlines are violated for most cases because of stochastic task execution times. Therefore, ElasticSim is a promising platform for evaluating the practical performance of workflow resource provisioning and scheduling algorithms, which supports resource runtime auto-scaling and stochastic task execution time modeling.
引用
收藏
页码:257 / 272
页数:16
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