.png){kind=icon}
返回
Evolutionary Multi-Objective Workflow Scheduling for Volatile Resources in the Cloud
.png){kind=icon}
PRE
AI
DOI：10.1109/TCC.2020.2993250.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The cloud has been widely used as a distributed computing platform for running scientific workflow applications. Most of the cloud providers encourage the use of their underutilized resources as spot instances for much cheaper prices compared with common resources as on-demand instances, however, the promise of lower costs for resources results in the volatility such that spot instances can be interrupted at any time by cloud providers. Many workflow scheduling algorithms have been proposed to deal with volatile resources. In this article, we consider the two most important features of the volatile resources namely fulfillment and interruption rates to fully model the instability of the cloud infrastructure. Subsequently, we propose a novel evolutionary multi-objective workflow scheduling approach to generate a set of trade-off solutions that outperform state-of-the-art algorithms in both makespan and economic costs. In addition, we explore the fluctuation of makespan and costs for our obtained schedules under different levels of fulfillment and interruption rates. Experimental results with the five well-known real-world workflows demonstrate that our evolutionary multi-objective workflow scheduling algorithm is competitive in terms of makespan and cost compared with state-of-the-art on-demand scheduling techniques.
Keyword:
Workflow scheduling
workflow makespan and cost
evolutionary algorithm
spot instance
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
I
IF:
5
论文数:
1.9K
被引数:
4.3K
机构
引用论文
Alternative Splicing of the DrosophilaDscamPre-mRNA Is Both Temporally and Spatially Regulated.png)
Genetics
OAAI
GeneticsIF0
OAAI分享收藏An efficient cost optimized scheduling for spot instances in heterogeneous cloud environment异构云环境下spot实例的高效成本优化调度
PREAI
分享收藏
查看更多内容