Behavior Reconstruction Models for Large-scale Network Service Systems

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Date: Mar. 2019
Publisher: Springer
Document Type: Report
Length: 285 words

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Byline: Zhaohui Zhang (1,2,3), Lina Ge (4), Pengwei Wang (1,2,3), Xinxin Zhou (1) Keywords: Large-scale network service system; Behavior membership function; Load balancing; Behavior reconstruction; Petri net Abstract: In large-scale network service systems, the phenomenon of instantaneous gathering of a large number of users can cause system abnormality, whenever the load imposed by the user behaviors does not match the system load. This paper proposes a behavior reconstruction model for large-scale network service systems integrated with Petri net reconstruction methodology, for the purpose of achieving load balancing in the system under increasing number of users. Based on the features of the user interaction behavior sequence, the behavioral load balancing model defines a user behavior membership function. Then, a random fuzzy Petri net with delay is presented to control the user behavior reconstruction. Experiments conducted by considering various changes in the number of user behaviors and their distribution in unit time demonstrate that the proposed methodology can effectively trigger the reconstructed model to balance the system load when the system load exceeds the defined warning point. Author Affiliation: (1) 0000 0004 1755 6355, grid.255169.c, School of Computer Science and Technology, Dong Hua University, Shanghai, China (2) 0000000123704535, grid.24516.34, The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai, China (3) Shanghai Engineering Research Center of Network Information Services, Shanghai, China (4) grid.440646.4, Department of Computer Science and Technology, Anhui Normal University, Wuhu, China Article History: Registration Date: 24/11/2017 Received Date: 27/08/2017 Accepted Date: 24/11/2017 Online Date: 02/01/2018 Article note: This article is part of the Topical Collection: Special Issue on Software Defined Networking: Trends, Challenges and Prospective Smart Solutions Guest Editors: Ahmed E. Kamal, Liangxiu Han, Sohail Jabbar, and Liu Lu

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Gale Document Number: GALE|A574708032