POWER MANAGEMENT AND AUTOMATIC RESOURCE PROVISIONING FRAMEWORK FOR CLOUD INFRASTRUCTURES BASED ON MACHINE LEARNING
DOI:
https://doi.org/10.61841/7q5b4m98Keywords:
Power management, Resource allocation, Cloud server,, virtual machines.Abstract
Power is becoming an increasingly important concern for large-scale cloud computing systems. Meanwhile, cloud service providers leverage virtualization technologies to facilitate service consolidation and enhance resource utilization. In a virtualized environment, resource needs to be configured at runtime at the cloud, server and virtual machine levels to achieve high power efficiency. In addition, cloud power management should guarantee high users’ SLA (service level agreement) satisfaction. This paper provides power management and automatic resource provisioning framework for cloud infrastructures based on machine learning. It is a proactive technique for power management and auto-scaling of resources that changes the number of resources for the private cloud dynamically based on system load is proposed. To configure cloud resources, we consider machine learning techniques to achieve automatic resource allocation and optimal power efficiency. The technique that supports both on-demand and advance reservation requests uses machine learning. Experimental results demonstrate that the proposed technique can effectively estimate the power usage of cloud servers and reduction of cost for the client enterprise. Additionally resource configuration approach achieves the lowest energy usage among the compared three approaches.
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[1] Wonok Kwon, Hagyoung Kim, “Efficient Server Power Supply Configuration for Cloud Computing Data Center”, 2014 International Conference on Computational Science and Computational Intelligence, Volume: 2, Year: 2014
[2] Jie Bao, Zhihui Lu, Jie Wu, Shiyong Zhang Yiping Zhong, “Implementing a novel load- aware auto scale scheme for private cloud resource management platform”, 2014 IEEE Network Operations and Management Symposium (NOMS), Year: 2014
[3] Nguyen Trung Hieu, Mario Di Francesco, Antti Ylä Jääski, “A virtual machine placement algorithm for balanced resource utilization in cloud data centers”, 2014 IEEE 7th International Conference on Cloud Computing, Year: 2014
[4] Wang En Dong, Wu Nan, Li Xu, “QoS-Oriented Monitoring Model of Cloud Computing Resources Availability”, 2013 International Conference on Computational and Information Sciences, Year: 2013
[5] Keting Yin, Shan Wang, Gang Wang, Zhengong Cai, Yixi Chen, “Optimizing deployment of VMs in cloud computing environment”, Proceedings of 2013 3rd International Conference on Computer Science and Network Technology, Year: 2013
[6] Sadeka, I. et al., “Empirical prediction models for adaptive resource provisioning in the cloud”, Future Generation Computer Systems, vol. 28, no. 1, pp 155 – 165, January, 2012
[7] G. Dhiman and T. S. Rosing, “ System-level power management using online learning”, Proceedings of the Computer-Aided Design of Integrated Circuits and Systems (CADICS), pp. 676–689, 2009.
[8] A. Verma, P. Ahuja, and A. Neogi, “pMapper: power and migration cost aware application placement in virtualized systems”, Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware, p.p 243–264, 2008.
[9] Wood, T. et al., “Profiling and Modeling Resource Usage of Virtualized Applications” Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware, New York, USA pp. 366-387, 2008.
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