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Published in: Cluster Computing 4/2020

12-02-2020

Satisfaction aware QoS-based bidirectional service mapping in cloud environment

Authors: Neeraj, Major Singh Goraya, Damanpreet Singh

Published in: Cluster Computing | Issue 4/2020

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Abstract

Due to large-scale growth in the number of service providers and consumers in cloud, their efficient mapping has become a complex undertaking. In most of the recent research works, quality of service (QoS) based selection of a service provider for a consumer has been recommended for service mapping. This is a unidirectional approach where the mapping of service is based on the service providers’ evaluation in the context of QoS requirements of a consumer. But, in the business perspective of cloud, the bidirectional evaluation of the participating entities (service providers and consumers) in the mapping process is necessary for increasing their service satisfaction. Therefore, this paper proposes a mutual evaluation-based cloud service mapping (MECSM) framework which addresses the bidirectional evaluation of both the service providers and consumers. MECSM framework uses the Analytic Hierarchy Process method to evaluate the service providers and standard RFM (Recency, Frequency, and Monetary) model to evaluate the consumers. A mathematical model is evolved to draw the service satisfaction of the service provider and consumer involved in a service transaction. The process of service mapping is depicted through a case study. The stability of the MECSM framework is validated by performing the sensitivity analysis. For performance analysis, the scaling range of service providers and consumers in a controlled overhead is obtained through the extensive simulation experiments. A comparison of results with the existing service mapping frameworks proves its better performance in the cloud.

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Metadata
Title
Satisfaction aware QoS-based bidirectional service mapping in cloud environment
Authors
Neeraj
Major Singh Goraya
Damanpreet Singh
Publication date
12-02-2020
Publisher
Springer US
Published in
Cluster Computing / Issue 4/2020
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-020-03065-7

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