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An Analytical Model for Dynamic Resource Allocation Framework in Cloud Environment

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Author Affiliations

  • 1 Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Lucknow, UP, INDIA

Res. J. Recent Sci., Volume 3, Issue (IVC-2014), Pages 1-6,(2014)

Abstract

Cloud computing has emerged as the most popular paradigm for on-demand, pay-per-use model of computing. The software, platform and infrastructure as a service model will become the most popular mode of getting computing resources by common users. There has been growing research interest in managing the cloud of resources so as to achieve optimum utilization of resources along with desired quality of service. In the present scenario there is much scope of research in mapping users’ request to appropriate servers in cloud computing environment. In this paper, the authors propose an analytical model that maps dynamic users’ request to physical servers in the cloud that is based on a fixed charge mutli-index transportation problem. Thus a multi-index transportation Problem Cloud Resource Scheduler (MTPCRS) mechanism with mathematical formulation is developed along with a numerical example. A Multi- Indexed Cloud Resource Scheduling Algorithm (MICRSA) is also given in order to calculate the total cost of processing the service requests. With the help of sequence diagram and business process diagram it is shown that the model is simple to implement and produces an efficient and cost effective resource allocation plan for satisfying users’ requests.

References

  1. Anton B., Jemal A. and Buyya R., Energy-aware Resource Allocation Heuristics for Efficient Management of Data Centers for Cloud Computing, Journal of Future Generation Computer Systems, 755–768, (2012)
  2. Anton B. and Buyya R., Optimal Online DeterministicAlgorithms and Adaptive Heuristics for Energy and Performance Efficient Dynamic Consolidation of Virtual Machines in Cloud Data Centers, Concurrency And Computation: Practice And Experience, 24, 1397–1420,(2012)
  3. Johan T., Montero Rubén S., Moreno-Vozmediano Rafael, Llorente Ignacio M., Cloud Brokering Mechanisms For Optimized Placement Of Virtual Machines Across Multiple Providers, Future Generation Computer Systems,28, 358–367, (2012)
  4. Mahmoodi K.R., Nejad S.S. and Ershadi M., ‘Expert Systems and Artificial Intelligence Capabilities Empower Strategic Decisions: A Case study, Res. J. Recent Sci.,3(1), 116-121, (2014)
  5. Liu B., Uncertain Programing, Wiley, New York, (1999)
  6. Buyya R., Garg S.K. and Calheiros R.N., ‘SLA Oriented Resource Provisioning for Cloud Computing: Challenges, Architecture and Solutions, Proceedings of the International Conference on Cloud and Service Computing, IEEE, Australia, 1-10, (2011)
  7. Yang L. and Feng Y., A Bicriteria Solid Transportation Problem With Fixed Charge Under Stochastic Environment, Applied Mathematical Modelling,31, 2668-2683, (2007)
  8. Senthil K. and Balasubramanie P., ‘Dynamic Scheduling for Cloud Reliability using Transportation Problem,Journal of Computer Science’,8(10), 1615-1626, (2012)
  9. Aneja Y.P. and Nair K.P.K., ‘Bicriteria Transportation Problems, Management Science,25, 73-78, (1979)
  10. Movahedi M.M, ‘A Statistical Method for Designing and analyzing tolerances of Unidentified Distributions,Res. J. Recent Sci.,2(11), 55-64, (2013)
  11. Bautista L. and Abran A., Design of A Performance Measurement Framework For Cloud Computing, J. Software Eng. Appli.,5, 69-75, (2012)
  12. Marston S., Li Z., Subhajyoti B., Zhang J. and Ghalsasi A., Cloud Computing-The business perspective, Decision Support Systems, 51, 176–189 (2011)
  13. Nathuji R. and Schwan K., Virtual Power: Coordinated Power Management in Virtualized Enterprise Systems,ACM SIGOPS Operating Systems Review,41(6), 265–278,(2007)
  14. Mahmoodi K.R., Nejad S.S. and Ershadi M., Expert Systems and Artificial Intelligence Capabilities Empower Strategic Decisions: A Case study, Res. J. Recent Sci.,3(1), 116-121, (2014)