Task scheduling optimization strategy using improved ant colony optimization algorithm in cloud computing
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ORIGINAL RESEARCH
Task scheduling optimization strategy using improved ant colony optimization algorithm in cloud computing Xianyong Wei1 Received: 15 July 2020 / Accepted: 10 October 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract In order to solve the problems of unbalanced load, slow convergence speed and low utilization of virtual machine resources existing in the previous task scheduling optimization strategies, this paper proposes a task scheduling optimization strategy using improved ant colony optimization algorithm in cloud computing. Firstly, based on the principle of cloud computing task scheduling, a scheduling model using improved ant colony algorithm is proposed to avoid the optimization strategy falling into local optimization. Then, task scheduling satisfaction function is constructed by combining the three objectives of the shortest waiting time, the degree of resource load balance and the cost of task completion to search the optimal solution of task scheduling. Finally, the reward and punishment coefficient is introduced to optimize the pheromone updating rules of ant colony algorithm, which speeds up the solution speed. Besides, we use dynamic update of volatility coefficient to optimize overall performance of this strategy, and introduce virtual machine load weight coefficient in the process of local pheromone updating, so as to ensure the load balance of virtual machine. The feasibility of our algorithm is analyzed and demonstrated by experiments with Cloudsim. The experimental results show that the proposed algorithm has the fastest convergence speed, the shortest completion time, the most balanced load and the highest utilization rate of virtual machine resources compared with other methods. Therefore, our proposed task scheduling optimization strategy has the best performance. Keywords Cloud computing · Task scheduling optimization · Improved ant colony algorithm · Load balancing · Penalty coefficient · Cloudsim
1 Introduction Cloud computing is a new revolution in the development of Internet computing. Compared with distributed computing, it has more outstanding advantages. It is a highly efficient multifunctional computing system, and large-scale computing resources ensure the efficiency of cloud services (Yuan et al. 2019). The tasks that need to be processed in cloud computing are scheduled and distributed among various computing resources. Users can obtain computing and information services and efficient storage services according to their needs by cloud computing (Guo et al. 2019; Hung et al. 2019). As the core technology of cloud computing, task scheduling is one of important links in the process of cloud computing processing tasks. Therefore, optimizing task
* Xianyong Wei [email protected] 1
Shangqiu Polytechnic, Shangqiu 476000, Henan, China
scheduling mechanisms is an important method to enhance the comprehensive performance of cloud computing (Garg and Chaurasia 2019). Cloud computing integrates and manages a large number of idle comput
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