Adaptive context‐aware service optimization in mobile cloud computing accounting for security aspects

Abstract

SummaryIn this article, we present an original agent‐based adaptive task scheduling system which optimizes the performance of services in the mobile cloud computing environment using machine learning mechanisms and context information. The system learns how to allocate resources appropriately: how to schedule services/tasks optimally between the mobile device and the cloud. Decisions are made taking into account the context (e.g., network connection type, location, security level). In this study, a supervised learning agent architecture and service selection algorithm are proposed to solve this problem. Adaptation is performed online on a mobile device. To verify the solution proposed, appropriate software has been developed and a series of experiments has been conducted. Results demonstrate that owing to the experience gathered and the learning process performed, the decision module becomes more efficient in assigning the task to either the mobile device or cloud resources. In the face of presented improvements, the security issues inherent in the context of mobile services/applications and cloud computing are further discussed. As threats associated with mobile data offloading are a serious concern, often ruling out the utilization of cloud services, we propose a more security focused approach for our solution, preferably without hindering performance.