Open Access

ARTICLE

Exploration on the Load Balancing Technique for Platform of Internet of Things

by Donglei Lu¹, Dongjie Zhu^2,*, Yundong Sun³, Haiwen Du³, Xiaofang Li⁴, Rongning Qu⁴, Yansong Wang³, Ning Cao¹, Helen Min Zhou⁵

1 School of Artificial Intelligence, Wuxi Vocational College of Science and Technology, Wuxi, 214028, China
2 School of Computer Science and Technology, Harbin Institute of Technology, Weihai, 264209, China
3 School of Astronautics, Harbin Institute of Technology, Harbin, 150001, China
4 Department of Mathematics, Harbin Institute of Technology, Weihai, 264209, China
5 School of Engineering, Manukau Institute of Technology, Auckland, 2241, New Zealand

* Corresponding Author: Dongjie Zhu. Email:

Computer Systems Science and Engineering 2021, 38(3), 339-350. https://doi.org/10.32604/csse.2021.016683

Received 08 January 2021; Accepted 27 February 2021; Issue published 19 May 2021

Abstract

In recent years, the Internet of Things technology has developed rapidly, and smart Internet of Things devices have also been widely popularized. A large amount of data is generated every moment. Now we are in the era of big data in the Internet of Things. The rapid growth of massive data has brought great challenges to storage technology, which cannot be well coped with by traditional storage technology. The demand for massive data storage has given birth to cloud storage technology. Load balancing technology plays an important role in improving the performance and resource utilization of cloud storage systems. Therefore, it is of great practical significance to study how to improve the performance and resource utilization of cloud storage systems through load balancing technology. On the basis of studying the read strategy of Swift, this article proposes a reread strategy based on load balancing of storage resources to solve the problem of unbalanced read load between interruptions caused by random data copying in Swift. The storage asynchronously tracks the I/O conversion to select the storage with the smallest load for asynchronous reading. The experimental results indicate that the proposed strategy can achieve a better load balancing state in terms of storage I/O utilization and CPU utilization than the random read strategy index of Swift.

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Keywords

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