Reinforcement learning based methodology for energy-efficient resource allocation in cloud data centers | Zendy

Thandar Thein | Zendy; Myint Myat Myo | Zendy; Sazia Parvin | Zendy; Amjad Gawanmeh | Zendy

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Reinforcement learning based methodology for energy-efficient resource allocation in cloud data centers

Author(s) -

Thandar Thein,

Myint Myat Myo,

Sazia Parvin,

Amjad Gawanmeh

Publication year - 2018

Publication title -

journal of king saud university - computer and information sciences

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.617

H-Index - 33

eISSN - 2213-1248

pISSN - 1319-1578

DOI - 10.1016/j.jksuci.2018.11.005

Subject(s) - cloud computing , planetlab , computer science , data center , efficient energy use , resource allocation , reinforcement learning , software deployment , distributed computing , database , environmental economics , the internet , computer network , operating system , engineering , artificial intelligence , electrical engineering , economics

Energy-efficient Cloud Infrastructure Resource Allocation Framework is getting popularity as it is paying effective attention to cloud data management with a view to achieve maximize revenue and minimize cost. This infrastructure can encourage for both cloud providers and users for allocating cloud infrastructure resources for fulfilling not only good energy efficiency measured in Power Usage Effectiveness (PUE) and data center Infrastructure Efficiency (DCiE) but also high CPU utilization. Therefore, in this paper we proposed a framework which can show effective performance for achieving the high data center energy efficiency and preventing Service Level Agreement (SLA) violation respectively with the aim of green cloud resources deployment. The framework accomplishes cloud infrastructure resource allocation on the basic of Reinforcement Learning mechanism and Fuzzy Logic for green solutions. The evaluation for Energy-efficient Resource Allocation is experimented on the traces of the PlanetLab virtualized environment for gaining good PUE and CPU utilization.

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