A comparative analysis of prominently used MCDM methods in cloud environment

Incloud computing, the selection of an efficient multi-criteria decision-making (MCDM) method (with minimum time complexity and maximum robustness) is a challenging and interesting problem. The time complexity and robustness of a MCDM method depend upon the methodology of evaluating the best alternative (i.e., cloud service). Although numerous MCDM methods are proposed for the quality-of-service based service selection in the cloud, still the issue of selecting the most efficient method remains unresolved. This paper presents a comparative analysis of the prominently used MCDM methods in terms of time complexity and robustness. The MCDM methods are used in the geographical region selection problem for Amazon Web Service cloud, and a comparative analysis of the obtained ranking results is performed. Further, application-specific analysis and sensitivity analysis are performed to ascertain the robustness of ranking methods. Experimental analysis is performed on the large-scale synthetic dataset to get the ranking overhead, i.e., time complexity of different MCDM methods.