Feedback-Based Swarm Optimization for Optimized Decision Making in Unsecured Mobile Cloud Coordinated Service

Abstract

Distributed applications are vulnerable to more unauthorized breaches in a service executing environment which runs trusted methods. The lack of security which is concerned with trust attributes decreases with many factors in upgraded trust at vendor’s end, and this leads to a lot of obstacles at vendor’s end for many of the deployments. A model which is really updated enough for executing the operations in trusted environment is required to reduce trust issues. To mitigate the risk level of security, the operational code executed in the trust wrap premises must be enabled with moderated or decreased trusted code. Processing the steps which are highly sensitive is handled in these environments only at trusted regions, whereas in untrusted part, the regions are not processed. Swarm intelligence (SI) is a technique which influences recent trends computing applications, and few of the SI techniques are particle swarm optimization (PSO) and ant colony optimization (ACO) which are revised for better optimization. Likewise, secure cloud services require a properly coordinated and decision-making system for a better-trusted transaction between cloud vendors and users. In this paper, we have implemented a decision-making system by deploying Boid-based feedback analysis that coordinates between its clusters that forward proper feedback for running the service on a trust lacking environment in mobile cloud nodes. Here we set a decision resulting layer under the distributed services to inject feedback results among the respective cluster. Therefore, an optimized-trusted approach is been introduced to manage the coordination between cloud providers.