Mobile Cloud Computing (MCC) helps increasing performance of intensive mobile applications by offloading heavy tasks to cloud computing infrastructures. The first step in this procedure is partitioning the application into small tasks and identifying those that are better suited for offloading. The method call partitioning strategy splits the code into a set of method calls that are offloaded to remote servers. Quite often, many applications need to make use of multiple servers for parallel processing of intensive computational operations. Predicting the behavior of such parallelizable applications is not an easy task. Deciding the number of remote servers determines the performance of the applications and the costs of the cloud usage. On one hand, users are interested in improving the performance of their applications, so they would like to use as many servers as possible, but on the other hand, they would also like to reduce their costs by using fewer cloud resources. In this paper, we propose a Stochastic Petri Net (SPN) modeling strategy to represent method call executions of mobile cloud systems. This approach enables a designer to plan and optimize MCC environments in which SPNs represent the system behavior and estimate the execution time of parallelizable applications.

One of the challenges the IT industry faces involves maintaining the quality of the developed product within a consistent planning. Product delays are in general consequences of badly conceived planning and lack of progress control. The interest in software testing activities has been growing over the last few years. Software companies currently seek low cost testing procedures and at the same, with great capacity to handle mistakes. In this article, concepts related to testing processes are mapped in Stochastic Petri Nets(SPN) which provides a formal representation for measures used in performance analysis.