Evaluation of cloud computing resource scheduling based on improved optimization algorithm
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ORIGINAL ARTICLE
Evaluation of cloud computing resource scheduling based on improved optimization algorithm Huafeng Yu1 Received: 26 February 2020 / Accepted: 30 May 2020 © The Author(s) 2020
Abstract Cloud computing, as a new computing mode in recent years, has been pursued by many users who have computational requirements, and the service quality of cloud computing depends largely on the efficiency of resource scheduling. In this study, an improved particle swarm optimization (IPSO) algorithm was proposed to improve the efficiency of resource scheduling, and simulation experiments were carried out on the IPSO algorithm and the traditional particle swarm optimization using CloudSim simulation platform. The phenomenon of premature appeared with the increase of the number of iterations, and the globally optimal solution was not found. The IPSO algorithm was more efficient in exploring the globally optimal solution, and the phenomenon of premature did not appear. As the number of tasks increased, the operation time of both algorithms increased, but the IPSO algorithm increased more slowly. The IPSO algorithm had more advantages when there were a large amount of tasks. Virtual machines in the two algorithms had different loads, and the load of the virtual machine in the IPSO algorithm was more balanced. Keywords Cloud computing · Improved particle swarm algorithm · CloudSim · Resource scheduling
Introduction As a new technology, cloud computing can accomplish the computing resource service using an easy extension method according to the user’s need for computing [1]. Users can access various services provided by cloud computing through the Internet. Unlike traditional information technology patterns, cloud computing services do not require users to install and operate applications on their own, thus reducing the cost required by user services. Besides, the resources and operations required by users are on the cloud terminal of the Internet; as a result, the operation load on the user terminal is greatly reduced, which reduces the requirement on the configuration of computer and saves the cost of users. With the development of computer information technology, users’ demands for cloud computing platform are also increasing. How to schedule resources based on demands has become a major problem. Bölöni et al. [2] made cloud * Huafeng Yu [email protected] 1
School of Digital Information Technology, Zhejiang Technical Institute of Economics, Hangzhou 310018, Zhejiang, China
computing analysis of real estate investment opportunities based on scheduling information value and found that the operation can be greatly simplified. Chen et al. [3] proposed an improved ant colony system (IACS) method and carried out extensive experiments based on workflows with different sizes and different cloud resources. The experimental results showed that IACS could find better solutions and was more cost-effective than basic particle swarm algorithm and dynamic object genetic algorithm under different scheduling scales and time limits.
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