Geometric Mean based Algorithm for Prioritizing Processors on Cloud Environment

Abstract

Cloud computing relates to the bunch of services that are provided to the customers on lease, by the servers located at different sites over the internet. The servers have pool of resources that can be scaled up and down on the basis of requirement. This results into communication and computation over the network. Divisible load theory has become popular during the past two decades. Based on divisible load theory the computations and communications can be divided into some arbitrarily independent parts and each part can be processed independently by a processor. The fraction of load must be allocated the processors based on some priorities. Analytical Hierarchy Process(AHP) is a multi-criteria based technique used for assigning priorities to the processors. Existing approach can handle the priority of processors using Eigen Value method of Analytical Hierarchy Process. The proposed model works on Geometric mean method of Analytical Hierarchy Process in order to improve parameters such as makespan, average response time and average waiting time.

A subjective and objective fuzzy-based analytical hierarchy process model for prioritization of lean product development practices

“…However, among the approaches that have found prominent application in the literature include Chang's extent analysis and the row geometric mean method. The main philosophy behind this theory and approaches are expressed and supported with real case applications in (Chang, 1996;Parmar & Bhoi, 2015), here the triangular fuzzy numbers are used for the fuzzy AHP pairwise comparison scale, while the extent analysis method is used for calculating the synthetic extent values of the pairwise comparisons. Other approaches not mention in (Kahraman et al, 2004) includes, the Saaty's evaluation approach which uses the principal eigenvector of pairwise comparison matrix which is derived from the scaling ratio used for finding comparative weight among the criteria (Kong & Liu, 2005;Saaty, 2008), and the fuzzy AHP row arithmetic mean method for fuzzy weight evaluation which uses fuzzified and defuzzified approach to obtain the priority weight.…”