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Self-Optimization in Cloud Computing Considering Reliability and Energy

Volume 13, Number 2, March 2017 - SC 69 pp. 240-244
DOI: 10.23940/ijpe.17.02.p13.240244


School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China

(Received on September 5, 2016; Revised on November 24, 2016 and February 14, 2017)


In the virtual data-center, how to map virtual machines (VMs) to physical machines (PMs) is becoming a hot issue. However, most of existing VM scheduling schemes have not fully considered the reliability and dynamical workloads of VMs. This paper presents a novel bionic autonomic nervous system (BANS) based approach for cloud resource management. This approach supports self-optimization that provides a dynamic and autonomic way to adapt to dynamical workloads and VM resource requirements. For the VM allocation in the self-optimization, this paper presents a reliability-performance-energy correlation model that can model, analyze and evaluate reliability, performance and power consumption simultaneously.


References: 4

[1].         Zhang, Q., M.F. Zhani, M. Jabri, and R. Boutaba. Venice: Reliable Virtual Data Center Embedding in Clouds. In INFOCOM, 2014 Proceedings IEEE 2014 Apr 27:289-297.

[2].         Tighe, M., G. Keller, M. Bauer, and H. Lutfiyya. A Distributed Approach to Dynamic VM Management. International Conference on Network and Service Management, Oct 14, 2013;166-170.

[3].         Chen, L., H. Shen, and K. Sapra. (2014). Rial: Resource Intensity Aware Load Balancing in Clouds. INFOCOM, 2014, Proceedings IEEE Apr 27, 2014;1294-1302.

[4].         Xiao, Z., W. Song, and Q. Chen. Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment. IEEE Transactions on Parallel and Distributed Systems, June 2013;24(6):1107-17.


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