Resource Management in Large Cloud Environments Using Lyapunov and Heuristic Algorithm

Authors

  • Therasa.T Computer Science and Engineering, India
  • V. Vasanth Computer Science and Engineering, India
  • Vidhya. M Computer Science and Engineering, India

Keywords:

Cloud Computing, Distributed Management, Resource Allocation, Lyapunov Optimization

Abstract

Powerful source management for a large-scale reasoning environment is a significant issue. Power consumption consists of a significant fraction of total operating price in information facilities. Resource allowance among sites/applications, dynamically adjusts the allowance to load changes and machines both in the number of physical machines and sites/applications. A method that determines an optimal remedy without considering memory restrictions and prove correctness and unity properties. Increase that method to provide an efficient heuristic remedy for the complete issue, which includes reducing the price for adjusting an allowance. The method consistently carries out on dynamic, local input and does not require global synchronization, the heuristic criteria to do. Utilizing available power storage space capability in information facilities to reduce electric bill under real-time electricity market. Lyapunov optimization technique is applied to design a criterion that accomplishes an precise compromise between price preserving and power storage space capacity.

References

R. Yanggratoke, F. Wuhib, and R. Stadler, “Gossip-based resource allocation for green computing in large clouds,” in 2011 International Conference on Network and Service Management.

OpenNebula Project Leads, http://www.opennebula.org/, Feb. 2012.

OpenStack LLC, http://www.openstack.org, Feb. 2012.

Eucalyptus Systems, Inc., http://www.eucalyptus.com/, Feb. 2012.

UC Santa Barbara, http://appscale.cs.ucsb.edu/, Feb. 2012.

“IBM WebSphere Application Server,” http://www.ibm.com/software/ webservers/appserv/extend/virtualenterprise/, Feb. 2012.

VMWare, http://www.cloudfoundry.com/, Feb. 2012.

Amazon Web Services LLC, http://aws.amazon.com/ec2/, Feb. 2012.

Google Inc., http://code.google.com/appengine/, Feb. 2012.

Y. Guo, Z. Ding, Y. Fang, and D. Wu, “Cutting Down Electricity Cost in Internet Data Centers by Using Energy Storage,” Proc. IEEE GLOBECOM ’11, Dec. 2011.

M. Armbrust, A. Fox, R. Griffith, A.D. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, and M. Zaharia, “A View of Cloud Computing,” Comm. ACM, vol. 53, no. 4, pp. 50-58, Apr. 2010.

A. Qureshi, R. Weber, H. Balakrishnan, J. Guttag, and B. Maggs, “Cutting the Electric Bill for Internet-Scale Systems,” ACM SIGCOMM Computer Comm. Rev., vol. 39, no. 4, pp. 123-134, Aug. 2009.

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Published

2014-02-28

How to Cite

[1]
Therasa.T, V. Vasanth, and Vidhya. M, “Resource Management in Large Cloud Environments Using Lyapunov and Heuristic Algorithm”, Int. J. Comp. Sci. Eng., vol. 2, no. 2, pp. 49–51, Feb. 2014.

Issue

Vol. 2 No. 2 (2014): IJCSE February Edition

Section

Research Article

License

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