Mobile Cloud Performance Evaluation Using Stochastic Models - 2018


Mobile Cloud Computing (MCC) helps increasing performance of intensive mobile applications by offloading serious tasks to Cloud Computing infrastructures. The first step in this procedure is partitioning the applying into small tasks and identifying those that are higher fitted to offloading. The strategy call partitioning strategy splits the code into a set of methodology calls that are offloaded to remote servers. Quite typically, several applications would like to create use of multiple servers for parallel processing of intensive computational operations. Predicting the behavior of such parallelizable applications isn't an easy task. Deciding the number of remote servers determines the performance of the applications and the prices of the cloud usage. On one hand, users are fascinated by improving the performance of their applications, so they would love to use as many servers as possible, but on the opposite hand, they might additionally like to scale back their costs by using fewer cloud resources. In this Project, we propose a Stochastic Petri.Net (SPN) modeling strategy to represent methodology call executions of mobile cloud systems. This approach allows a designer to plan and optimize MCC environments in which SPNs represent the system behavior and estimate the execution time of parallelizable applications.

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