Task Scheduling Strategy Based on Queueing Model in Cloud Computing and Its Energy Consumption Optimization Analysis

Task Scheduling Strategy Based on Queueing Model in Cloud Computing and Its Energy Consumption Optimization Analysis

In order to reduce the energy consumption of the cloud computing system while ensuring the response performance of the cloud users, a task scheduling strategy is proposed. In the strategy, the local processor of the mobile device continues to work, the virtual machines in a physical machine sleep synchronously, and the virtual machines in different physical machine sleep asynchronously. For the heterogeneous physical machines in cloud computing, a queueing model with a synchronous multiple vacation is established. By using the quasi-birth-death process and the matrix geometric solution, the steady-state distribution of the queueing model is given, and the expressions of the average response time of the tasks and the average power of the system are derived. The numerical results show that there is a power-performance trade-off for cloud computing when setting the assigning probability of the tasks to the local processor. Through the improved whale optimization algorithm, the task scheduling strategy is optimized with the minimum system cost.