Management of Context-Aware Software Resources Deployed in a Cloud Environment For Improving Quality of Mobile Cloud Services

Abstract

In cloud computing environments, context information is continuously created by context providers and consumed by the applications on mobile devices. An important characteristic of cloud-based context aware services is meeting the service level agreements (SLAs) to deliver a certain quality of service (Qos), such as guarantees on response time or price. The response time to a request of context-aware software is affected by loading extensive context data from multiple resources on the chosen server. Therefore, the speed of such software would be decreased during execution time. Hence, proper scheduling of such services is indispensable because the customers are faced with time constraints. In this research, a new scheduling algorithm for context aware services is proposed which is based on classifying similar context consumers and dynamically scoring the requests to improve the performance of the server hosting highly-requested context-aware software while reducing costs of cloud provider. The approach is evaluated via simulation and comparison with gi-FIFO scheduling algorithm. Experimental results demonstrate the efficiency of the proposed approach.