Dynamic Scalable Stochastic Petri Net: A Novel Model for Designing and Analysis of Resource Scheduling in Cloud Computing

摘要

Performance evaluation of cloud computing systems studies the relationships among system configuration, system load, and performance indicators. However, such evaluation is not feasible by dint of measurement methods or simulation methods, due to the properties of cloud computing, such as large scale, diversity, and dynamics. To overcome those challenges, we present a novel Dynamic Scalable Stochastic PetriNet (DSSPN) to model and analyze the performance of cloud computing systems. DSSPN can not only clearly depict system dynamic behaviors in an intuitive and efficient way but also easily discover performance deficiencies and bottlenecks of systems. In this study, we further elaborate some properties of DSSPN. In addition, we improve fair scheduling taking into consideration job diversity and resource heterogeneity. To validate the improved algorithm and the applicability of DSSPN, we conduct extensive experiments through Stochastic Petri Net Package (SPNP). The performance results show that the improved algorithm is better than fair scheduling in some key performance indicators, such as average throughput, response time, and average completion time.