Open AccessAn Effiecient Approach for Resource Auto-Scaling in Cloud Environments
Author(s) -
Bahar Asgari,
Mostafa GhobaeiArani,
Sam Jabbehdari
Publication year - 2016
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v6i5.pp2415-2424
Subject(s) - provisioning , cloud computing , computer science , resource (disambiguation) , process (computing) , service (business) , distributed computing , markov decision process , markov process , computer network , operating system , economy , economics , statistics , mathematics
Cloud services have become more popular among users these days. Automatic resource provisioning for cloud services is one of the important challenges in cloud environments. In the cloud computing environment, resource providers shall offer required resources to users automatically without any limitations. It means whenever a user needs more resources, the required resources should be dedicated to the users without any problems. On the other hand, if resources are more than user’s needs extra resources should be turn off temporarily and turn back on whenever they needed. In this paper, we propose an automatic resource provisioning approach based on reinforcement learning for auto-scaling resources according to Markov Decision Process (MDP). Simulation Results show that the rate of Service Level Agreement (SLA) violation and stability that the proposed approach better performance compared to the similar approaches.