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