标题:A Reinforcement Learning Based Workflow Application Scheduling Approach in Dynamic Cloud Environment
作者:Wei, Yi ;Kudenko, Daniel ;Liu, Shijun ;Pan, Li ;Wu, Lei ;Meng, Xiangxu
通讯作者:Wei, Yi
作者机构:[Wei, Yi ;Liu, Shijun ;Pan, Li ;Wu, Lei ;Meng, Xiangxu ] School of Computer Science and Technology, Shandong University, Jinan, China;[Kudenko, Daniel 更多
会议名称:13th International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2017
会议日期:11 December 2017 through 13 December 2017
来源:Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
出版年:2018
卷:252
页码:120-131
DOI:10.1007/978-3-030-00916-8_12
关键词:Cloud computing; Infrastructure as a service; Markov decision process; Q-learning; Service composition
摘要:Workflow technology is an efficient means for constructing complex applications which involve multiple applications with different functions. In recent years, with the rapid development of cloud computing, deploying such workflow applications in cloud environment is becoming increasingly popular in many fields, such as scientific computing, big data analysis, collaborative design and manufacturing. In this context, how to schedule cloud-based workflow applications using heterogeneous and changing cloud resources is a formidable challenge. In this paper, we regard the service composition problem as a sequential decision making process and solve it by means of reinforcement learning. The experimental results demonstrate that our approach can find near-optimal solutions through continuous learning in the dynamic cloud market. © 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054785089&doi=10.1007%2f978-3-030-00916-8_12&partnerID=40&md5=fba11c289f221f7acc13567a507caab6
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