A Dynamic Cloud Task Scheduling Based on Queue Queue and Pre-processed Reinforcement Learning

• 1、School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044

Abstract：As one of the key issues in cloud computing, it is an extremely challenging research topic to manage and schedule cloud resources. To achieve lower task response time and higher resource utilization in heterogeneous cloud environment, a dynamic cloud task scheduling algorithm based on Queue Theory and Pre-processed Reinforcement Learning(QTPRL) is proposed in this paper to make task scheduling more energy efficient. Firstly, this method builds task scheduling model based on M/M/S queuing model which adopts the design of a single queue and multi-resource pool to submit tasks to the idle resource pool (physical machine) un-uniformly, reducing the length of waiting queue and the waiting time of tasks. On this basis, a dynamic priority is conducted in the related queues by combining the length, deadline and waiting time of tasks to complete pre-processing. Finally, a kind of reinforcement learning method is used to assign tasks to an idle virtual machine on different physical machines. Simulation experiment results show that our scheduling scheme can outperform on the average response time, resource utilization, and the energy consumption compared to most of traditional methods.

Keywords： cloud computing task scheduling queuing model reinforcement learning energy saving