Cloud based architectures present many challenges, ranging from poorly dimensioned hardware to lengthy wait times and resource balancing issues. Most of these problems can be mitigated by using proper performance evaluation techniques, depending on the accuracy of the model abstracting the real system. The adoption of a formalism over another to describe the considered infrastructure plays a crucial role in achieving this goal. In this sense, multiformalism proves itself to be a powerful modeling approach describing each system component according to the most suitable representation. This paper presents a novel modeling method oriented to the prediction of cloud architectures performance, suitable for joining the advantages of high level modeling abstractions and of the detail of a specialized simulator. Generalized Stochastic Petri Nets are used to describe the workload and the behavior of users and applications, while Cloudsim, a well known cloud infrastructure simulator, is adopted for the cloud part. A case study of a simplified Edge Computing application is presented to demonstrate the effectiveness of the proposed approach.
- Performance evaluation