GSPN-Based Reliability-Aware Performance Evaluation of IoT Services

With the growing popularity of Internet of Things (IoT) services being applied in several aspects of real-life applications, performance has become an important requirement. Meanwhile, the techniques for reliability enhancement such as virtual machine migration and recovery also have significant impact on end-to-end performance. This paper proposes a predictive approach of reliability-aware performance evaluation for recoverable IoT services using the modeling techniques of generalized stochastic Petri net (GSPN). Mathematical models formulating the dynamics of both server clusters and IoT systems are presented, and quantitative analyses of performance metrics are provided. Empirical experiments based on real-world data obtained from IoT services and cloud systems are conducted, and parameter settings as well as experimental results are discussed in detail.