Abstract
Software as a Service (SaaS) provided by cloud computing has recently gained widespread adoption. Because of increased competition in the SaaS market, it is essential for a SaaS provider to properly design its computing system. Significant gains can be achieved by efficiently clustering software applications. This paper focuses on the application grouping problem encountered in computer clustering in SaaS networks. We present integer programming formulations and propose an efficient solution procedure based on the column generation technique applied to the problem. The results of a comprehensive computational study show that our column generation-based approach performed very well for large problem instances with optimality gaps varying between 0.00 and 3.02% with an average of 0.98% compared to optimality gaps varying between 0.00 and 230.64% with an average of 99.08% using a standard branch and bound technique as implemented by a state-of-the-art commercial solver.
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References
Adenuga OA, Kekwaletswe RM, Adenuga OT (2018) A systematic literature review to uncover SaaS adoption issues by SMEs–reasons and solutions to the adoption problem. Database, 2018
Amiri A (2016) Application placement and backup service in computer clustering in Software as a Service (SaaS) networks. Comput Oper Res 69:48–55
Amiri A (2017) Application placement in computer clustering in software as a service (SaaS) networks. Inf Technol Manage 18:161–173
Dantzig G, Wolfe P (1960) Decomposition principle for linear programs. Oper Res 8:101–111
El-Haddadeh R (2020) Digital innovation dynamics influence on organisational adoption: the case of cloud computing services. Inf Syst Front 22(4):985–999
Gaast JP, Rietveld CA, Gabor AF, Zhang Y (2014) A Tabu search algorithm for application placement in computer clustering. Comput Oper Res 50:38–46
Galvão RD, Espejo LG, BoffeyB, (2000) A comparison of Lagrangean and surrogate relaxations for the maximal covering location problem. Eur J Oper Res 124(2):377–389
Gantz JF, Miller P (2016) The salesforce economy: enabling 1.9 million new jobs and $389 billion in new revenue over the next five years. White paper released by IDC
Gartner, Inc. (2018) Gartner says worldwide public cloud services market to grow 18 percent in 2017. URL http://www.gartner.com/newsroom/id/3616417. Accessed: May 2018
Gilmore P, Gomory R (1961) A linear programming approach to the cutting stock problem. Oper Res 9:849–859
Gilmore P, Gomory R (1963) A linear programming approach to the cutting stock problem—Part II. Oper Res 11:863–888
IBM ILOG CPLEX Optimization Studio 12.5, IBM (2012).
Kwok T, Mohindra A (2008) Resource calculations with constraints, and placement of tenants and instances for multi-tenant SaaS applications. In: Proceedings of the international conference on service-oriented computing pp 633–648
Lian JW, Yen DC, Wang YT (2014) An exploratory study to understand the critical factors affecting the decision to adopt cloud computing in Taiwan hospital. Int J Inf Manage 34(1):28–36
Mehrotra A, Trick MA (1996) A column generation approach for graph coloring. INFORMS J Comput 8(4):344–354
Oxford Economics (2014) The cloud grows up, www.oxfordeconomics.com/recent-releases/the-cloud-grows-up Accessed May 2018
Vance P (1998) Branch-and-price algorithms for the one-dimensional cutting stock problem. Comput Optim Appl 9(3):211–228
Vanderbeck F, Wolsey LA (1996) An exact algorithm for IP column generation. Oper Res Lett 19(4):151–159
Yang H, Kumara S, Bukkapatnam ST, Tsung F (2019) The internet of things for smart manufacturing: a review. IISE Trans 51(11):1190–1216
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Amiri, A. The application grouping problem in Software-as-a-Service (SaaS) networks. Inf Technol Manag 23, 125–137 (2022). https://doi.org/10.1007/s10799-021-00348-2
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DOI: https://doi.org/10.1007/s10799-021-00348-2