A many-objective optimized task allocation scheduling model in cloud computing
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A many-objective optimized task allocation scheduling model in cloud computing Jialei Xu1 · Zhixia Zhang1 · Zhaoming Hu1 · Lei Du1 · Xingjuan Cai1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract The characteristics of randomness, running style, and unpredictability of user requirements in the cloud environment, brings great challenges to task scheduling. Meanwhile, the scheduling efficiency of cloud task allocation is an important factor affecting cloud resource systems. Therefore, this paper takes into account the characteristics of tasks, systems and users, a many-objective task scheduling model was constructed in cloud computing. In order to better solve the proposed manyobjective task scheduling model, a reference vector guided evolutionary algorithm based on angle-penalty distance of normal distribution (RVEA-NDAPD) is proposed, and compared with the existing standard many-objective evolutionary algorithms (MaOEAs). Simulation results show that the algorithm can effectively improve the performance of the proposed model in cloud computing and obtain a suitable task allocation strategy. Keywords Cloud computing · Task allocation · Many-objective algorithm · Virtual machine
1 Introduction Computer networks continue to develop at a rapid rate, and virtual machine technology [1] is becoming more and more perfect. The network has already become popular in people’s work and life. However, with the number of users continues to grow, the variety of tasks submitted by users, and the uncertainty and randomness of the Internet [2] have also brought huge challenges to task scheduling [3, 4]. These problems attack the traditional task allocation scheduling. Therefore, it is necessary to establish an efficient task scheduling model in the cloud computing environment. The essence of cloud computing is to divide large tasks in the network into small and independent sub-tasks, and then send these tasks to virtual resources composed of largescale server groups for processing. Therefor, providing a good task allocation and resource scheduling strategy for the computing model will directly affect the efficiency and service quality of the entire scheduling system. Xingjuan Cai
[email protected] 1
Complex System and Computational Intelligence Laboratory, School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan, 030024, China
These issues are required to be resolved, so service providers of cloud computing platforms [5] need to customize different service strategies for different users. The practice of reasonably distributing tasks to the most suitable virtual machines maximizes the efficiency and balance of the entire cloud resource scheduling system Load, thereby increasing user satisfaction with the quality of the platform’s services. Many researchers conduct research on reasonable task allocation strategies in the cloud environment. For example, Lin [6] proposed a pre-assignment task scheduling strategy to be applied to the scheduling. The size of eac
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