A Demand Based Resource Provisioner for Cloud Infrastructure

  • Satendra SahuEmail author
  • Harshit Gupta
  • Sukhminder Singh
  • Soumya Kanti Ghosh
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 28)


Resource management in cloud environment poses unique challenges. Resources, in the form of virtual machines (VM) are to be provisioned on the fly while using the underlying infrastructure efficiently and still meeting the performance parameters. This involves collecting system resource statistics for decision making by other components of cloud environment. In this paper, the process of resource (VM) management in the cloud is mapped to demand based system wherein the VMs that require additional resources or need to relinquish their resources send requests to a centralized controller. Further, since resources are limited, dynamic resource allocation forms a classical optimization problem. This paper proposes a one-dimensional knapsack optimization solved using dynamic programming, to achieve efficient resource allocation. The performance of the proposed algorithm has been compared with brute force algorithm.


Cloud computing Operations research Dynamic programming Just in time Demand based model 


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  1. 1.
    Mell, P., Grance, T.: Perspectives on cloud computing and standards. Technical report, National Institute of Standards and Technology (NIST), Information Technology Laboratory (2009)Google Scholar
  2. 2.
    Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging it platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems 25(6), 599–616 (2009)CrossRefGoogle Scholar
  3. 3.
    Tierney, B., Aydt, R., Gunter, D., Smith, W., Swany, M., Taylor, V., Wolski, R.: A grid monitoring architecture. The Global Grid Forum Draft Recommendation, GWD-Perf-16-3 (2002)Google Scholar
  4. 4.
    Cooke, A., Gray, A., Nutt, W., Magowan, J., Oevers, M., Taylor, P.: The relational grid monitoring architecture: Mediating information about the grid. Journal of Grid Computing, 323–339 (2004)Google Scholar
  5. 5.
    Brandt, J., Gentile, A., Mayo, J., Pebay, P., Roe, D., Thompson, D., Wong, M.: Resource monitoring and management with ovis to enable hpc in cloud computing environments. In: Proceedings of the 2009 IEEE International Symposium on Parallel & Distributed Processing, IPDPS 2009, pp. 1–8. IEEE Computer Society, Washington, DC (2009)CrossRefGoogle Scholar
  6. 6.
    Yazir, Y., Matthews, C., Farahbod, R., Neville, S., Guitouni, A., Ganti, S., Coady, Y.: Dynamic resource allocation based on distributed multiple criteria decisions in computing cloud. In: 3rd International Conference on Cloud Computing, pp. 91–98 (2010)Google Scholar
  7. 7.
    Nurmi, D., Wolski, R., Grzegorczyk, C., Obertelli, G., Soman, S., Youseff, L., Zagorodnov, D.: The eucalyptus open-source cloud-computing system. In: 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, CCGRID 2009, pp. 124–131. IEEE (2009)Google Scholar
  8. 8.
    Kavanagh, D.: Typica: A java client library for a variety of amazon web services (2008),
  9. 9.
    Arlitt, M.F., Williamson, C.L.: Internet web servers: Workload characterization and performance implications. IEEE/ACM Transactions on Networking (ToN) 5(5), 631–645 (1997)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Satendra Sahu
    • 1
    Email author
  • Harshit Gupta
    • 2
  • Sukhminder Singh
    • 1
  • Soumya Kanti Ghosh
    • 1
  1. 1.School of Information TechnologyIndian Institute of TechnologyKharagpurIndia
  2. 2.Department of Computer Science and EngineeringIndian Institute of TechnologyKharagpurIndia

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