Adaptive Evaluation of Virtual Machine Placement and Migration Scheduling Algorithms Using Stochastic Petri Nets

More and more mobile applications rely on the combination of both mobile and cloud computing technology to bring out their full potential. The cloud is usually used for providing additional computing resources that cannot be handled efficiently by the mobile devices. Cloud usage, however, results in several challenges related to the management of virtualized resources. A large number of scheduling algorithms are proposed to balance between performance and cost of data center. Due to huge cost and time consuming of measure-based and simulation method, this paper proposes an adaptive method to evaluate scheduling algorithms. In this method, the virtual machine placement and migration process are modeled by using Stochastic Reward Nets. Different scheduling methods are described as reward functions to perform the adaptive evaluation. Two types of performance metrics are also discussed: one is about quality of service, such as system availability, mean waiting time, and mean service time, and the other is the cost of runtime, such as energy consumption and cost of migration. Compared to a simulation method, the analysis model in this paper only modifies the reward function for different scheduling algorithms and does not need to reconstruct the process. The numeric results suggest that it also has a good accuracy and can quantify the influence of scheduling algorithms on both quality of service and cost of runtime.