Energy optimization methods for Virtual Machine Placement in Cloud Data Center

Esha Barlaskar, N. Ajith Singh, Yumnam Jayanta

Abstract


The Information Technology industry has been upheaved by the influx of cloud computing. The extension of Cloud computing has resulted in the creation of huge data centers globally containing numbers of computers that consume large amounts of energy resulting in high operating costs. To reduce energy consumption providers must optimize resource usage by performing dynamic consolidation of virtual machines (VMs) in an efficient way. The problems of VM consolidation are host overload detection, host under-load detection, VM selection and VM placement. Each of the aforestated sub-problems must operate in an optimized manner to maintain the energy usage and performance. The process of VM placement has been focused in this work, and energy efficient, optimal virtual machine placement (E2OVMP) algorithm has been proposed. This minimizes the expenses for hosting virtual machines in a cloud provider environment in two different plans such as i) reservation and ii) on-demand plans, under future demand and price uncertainty. It also reduces energy consumption. E2OVMP algorithm makes a decision based on the gilt-edged solution of stochastic integer programming to lease resources from cloud IaaS providers. The performance of E2OVMP is evaluated by using CloudSim with inputs of planet lab workload. It minimized the user’s budget, number of VM migration resulting efficient energy consumption. It ensures a high level of constancy to the Service Level Agreements (SLA).

Keywords: Cloud resource management; virtualization; dynamic consolidation; stochastic integer programming (SIP)

*Cite as: Esha Barlaskar, N. Ajith Singh, Y. Jayanta Singh, “Energy optimization methods for Virtual Machine Placement
in Cloud Data Center†ADBU J.Engg.Tech., 1(2014) 0011401(7pp)


Full Text:

PDF

References


A.Beloglazov, R. Buyya, Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurr. Comput. Pract. Exp. 24(13), 1–24 (2011)

J. R. Birge, F. Louveaux, Introduction to Stochastic Programming, Springer-Verlag Newyork, Inc., 1997

R Buyya, C. Shin Yeo and S Venugopal, “Market- Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilitiesâ€, 10th IEEE Int. Conf. on High Performance Computing & Communications, IEEE Computer Society, 2008.

K.Mukherjee and G.Sahoo, “Green Cloud: An Algorithmic Approachâ€, Int. J. of Computer Apps, Vol9(9), 2010.

J. Hu, J. Gu, G.Sun, T. Zhao, A Strategy on Load Balancing of VM Resources in Cloud Computing Environment, IEEE 3rd Int. Symposium on Parallel Architectures, Algo &Program. pp.89, 2010.

N. Bobroff, A. Kochut, and K. Beaty, “ Dynamic placement of virtual machines for managing SLA violationsâ€, In Proc. Int. Symposium on Integrated Network Management ’,2007.

D. Barbagallo, E. Di Nitto, D. J. Dubois, and R. Mirandola, “A bio-inspired algorithm for energy optimization in a self-organizing data center,†In Proc. SOAR’09. Berlin, pp. 127–151, 2010.

A. Beloglazovy, R. Buyya,Y. ChoonLee,A. Omaya, “A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems“, Tech. Report, CLOUDS-TR-2010-3, The University of Melbourne, 2010.

Nathuji R, Schwan K, “Virtual power: coordinated power management in virtualized enterprise systemsâ€,ACM,pp265, 2007.

A. Singh and M. Hemalatha, "Virtual Machine Placement by Using Honey Bee Forager Algorithm in Cloud Computing", Karpagam Journal of Computer Science, Vol7(4),pp 209, 2013.

B. Addis, D. Ardagna, B. Panicucci, M.Squillante, Li Zhang: “A Hierarchical Approach for the Resource Management of Very Large Cloud Platformsâ€, IEEE Tran. On Dependable &Secure Comp, 2013.

M. Sharifi, H.Salimi, M. Najafzadeh, “Power-efï¬cient distributed scheduling of virtual machines using workload-aware consolidation techniques“, Journal of Supercomputing, Vol 61(1), pp. 46, 2011.

M. Cardosa, M. R. Korupolu, and A. Singh, “Shares and Utilities based Power Consolidation in Virtualized Server Environments,†in Proc. of IEEE Integrated Network Management (IM),2009.

F. Hermenier, X. Lorca, and J.-M. Menaud, “Entropy: A Consolidation Manager for Clusters,†in Proc.ACM SIGPLAN/ SIGOPS Int. Conf. on Virtual Execution Environments, 2009.

W. Vogels, “Beyond Server Consolidationâ€, ACM QUEUE, vol. 6, no.1, pp. 20-26, January/Febuary 2008.

D. Nurmi, R. Wolski, and J. Brevik, “VARQ: Virtual Advance Reservations for Queuesâ€, in International Symposium on High Performance Distributed Computing, 2008.

A. Filali, A.S. Hafid, and M. Gendreau, “Adaptive Resources Provisioning for Grid Applications and Servicesâ€, in IEEE International Conference on Communications, 2008.

D. Kusic and N. Kandasamy, “Risk-Aware Limited Lookahead Control for Dynamic Resource Provisioning in Enterprise Computing Systemsâ€, in IEEE Int. Conference on Autonomic Computing, 2006.

S. Chaisiri, B. Lee, and D. Niyato. Optimal Virtual Machine Placement across Multiple Cloud Providers, Service Comp. Conf. APSCC 2009

Y. Jayanta Singh, Y. Somananda Singh, and S C Melhotra, “Dynamic Management of Transactions in Distributed Real- Time Processing Systemâ€, Int. J of Database Management System(IJDMS), Vol. 2, No. 2 , pp. 161-170, May, 2010

The SPECpower benchmark website.[Online].2013


Refbacks

  • There are currently no refbacks.


------------------------------------------------------------------------------------------------------------------------

The ADBU Journal of Engineering Technology (AJET)" ISSN:2348-7305

This journal is published under the terms of the Creative Commons Attribution (CC-BY) (http://creativecommons.org/licenses/)

Number of Visitors to this Journal: