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Autonomic decentralized elasticity based on a reinforcement learning controller for cloud applications
Nouri, Seyed Mohammad Reza; Li, Han; Venugopal, Srikumar1; Guo, Wenxia2; He, MingYun2; Tian, Wenhong2,3
2019-05-01
摘要Web applications have stringent performance requirements that are sometimes violated during periods of high demand due to lack of resources. Infrastructure as a Service (laaS) providers have made it easy to provision and terminate compute resources on demand. However, there is a need for a control mechanism that is able to provision resources and create multiple instances of a web application in response to excess load events. In this paper, we propose and implement a reinforcement learning-based controller that is able to respond to volatile and complex arrival patterns through a set of simple states and actions. The controller is implemented within a distributed architecture that is able to not only scale up quickly to meet rising demand but also scale down by shutting down excess servers to save on ongoing costs. We evaluate this decentralized control mechanism using workloads from real-world use cases and demonstrate that it reduces SLA violations while minimizing cost of provisioning infrastructure. (C) 2018 Elsevier B.V. All rights reserved.
关键词Cloud computing Autonomic decentralized elasticity Reinforcement learning Resource provisioning Cloud applications
DOI10.1016/j.future.2018.11.049
发表期刊FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
ISSN0167-739X
卷号94页码:765-780
通讯作者Tian, Wenhong(tian_wenhong@uestc.edu.cn)
收录类别SCI
WOS记录号WOS:000460845200061
语种英语