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Wireless Communications and Mobile Computing
Volume 2018, Article ID 7308913, 9 pages
Research Article

Method of Resource Estimation Based on QoS in Edge Computing

School of Information Science and Engineering, Qufu Normal University, Rizhao 276800, China

Correspondence should be addressed to Guangshun Li; moc.qq@58525703

Received 13 October 2017; Accepted 21 December 2017; Published 22 January 2018

Academic Editor: Shangguang Wang

Copyright © 2018 Guangshun Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


With the development of Internet of Things, the number of network devices is increasing, and the cloud data center load increases; some delay-sensitive services cannot be responded to timely, which results in a decreased quality of service (QoS). In this paper, we propose a method of resource estimation based on QoS in edge computing to solve this problem. Firstly, the resources are classified and matched according to the weighted Euclidean distance similarity. The penalty factor and Grey incidence matrix are introduced to correct the similarity matching function. Then, we use regression-Markov chain prediction method to analyze the change of the load state of the candidate resources and select the suitable resource. Finally, we analyze the precision and recall of the matching method through simulation experiment, validate the effectiveness of the matching method, and prove that regression-Markov chain prediction method can improve the prediction accuracy.