A policy-based location-aware framework for personalized services in cloud computing systems

Autonomous service adaptation in cloud environments requires both location-awareness and the acquisition and utilization of contextual information. Statically configured service adaptation frameworks lack the ability to adapt to changing network conditions and geographical locations. This paper presents a framework that continuously derives updated configuration policies with respect to service selection and handover to third party cloud service providers. Location tracking and prediction empower the system to provide ongoing robust services for cloud service subscribers. To achieve this goal, the proposed work relies on a policy-based real-time simulator to evaluate possible new service provider handover configurations before actually applying them to the real network. Preliminary performance evaluation results demonstrate the significant enhancement of the outcome of the proposed framework in terms of continuous services for subscribers and load balancing for service providers.