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Application placement in computer clustering in software as a service (SaaS) networks

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  • Ali Amiri

    (Oklahoma State University)

Abstract

One major service provided by cloud computing is Software as a Service (SaaS). As competition in the SaaS market intensifies, it becomes imperative for a SaaS provider to design and configure its computing system properly. This paper studies the application placement problem encountered in computer clustering in SaaS networks. This problem involves deciding which software applications to install on each computer cluster of the provider and how to assign customers to the clusters in order to minimize total cost. Given the complexity of the problem, we propose two algorithms to solve it. The first one is a probabilistic greedy algorithm which includes randomization and perturbation features to avoid getting trapped in a local optimum. The second algorithm is based on a reformulation of the problem where each cluster is to be assigned an application configuration from a properly generated subset of configurations. We conducted an extensive computational study using large data sets with up to 300 customers and 50 applications. The results show that both algorithms outperform a standard branch-and-bound procedure for problem instances with large sizes. The probabilistic greedy algorithm is shown to be the most efficient in solving the problem.

Suggested Citation

  • Ali Amiri, 2017. "Application placement in computer clustering in software as a service (SaaS) networks," Information Technology and Management, Springer, vol. 18(2), pages 161-173, June.
    • Handle: RePEc:spr:infotm:v:18:y:2017:i:2:d:10.1007_s10799-016-0261-9
    DOI: 10.1007/s10799-016-0261-9
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    References listed on IDEAS

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    1. Lan, Guanghui & DePuy, Gail W. & Whitehouse, Gary E., 2007. "An effective and simple heuristic for the set covering problem," European Journal of Operational Research, Elsevier, vol. 176(3), pages 1387-1403, February.
    2. Mazzola, Joseph B. & Neebe, Alan W., 1999. "Lagrangian-relaxation-based solution procedures for a multiproduct capacitated facility location problem with choice of facility type," European Journal of Operational Research, Elsevier, vol. 115(2), pages 285-299, June.
    3. M Haouari & J S Chaouachi, 2002. "A probabilistic greedy search algorithm for combinatorial optimisation with application to the set covering problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(7), pages 792-799, July.
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    Cited by:

    1. Hsin-Pin Fu & Tsung-Sheng Chang & Hsiao-Ping Yeh & Yu-Xuan Chen, 2019. "Analysis of Factors Influencing Hospitals’ Implementation of a Green E-Procurement System Using a Cloud Model," IJERPH, MDPI, vol. 16(24), pages 1-16, December.
    2. Ali Amiri, 2022. "The application grouping problem in Software-as-a-Service (SaaS) networks," Information Technology and Management, Springer, vol. 23(2), pages 125-137, June.

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