Gaussian Flower Pollination Optimization Based Trusted Service Selection for Cloud Computing Environment

Abstract

As cloud computing is a combination of variety of technologies, there are a number of security concerns need to be addressed. To protect from various attacks and to improve the security of cloud users, it is required to analyze the trust relationships among the cloud resources and tasks. Most of the trust management systems did not consider the significance of interactions, which affects the correctness of trust evaluations. This paper proposes Gaussian flower pollination optimization (GFPO) based trusted service selection (TSS) technique. In this technique, each cloud service provider's reputation values are computed from the trust value of its servers. The individual trust values of cloud servers are determined based on interaction success rate (ISR), service success index (SSI) and service response time (SRT) parameters. The optimal weight values of these parameters are adaptively determined using GFPO algorithm. During the data transfer from server to the user, the user checks the server's trust value and ignores the response if its trust value is low. Experimental results from Cloudsim show that GFPO-TSS minimizes the computation overhead and access delay, maximizes the accuracy and success rate. GFPO-TSS has 45% reduced computation cost, 10% increased data correctness.