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02.01.2018 | Ausgabe 2/2019 Open Access

Peer-to-Peer Networking and Applications 2/2019

Behavior Reconstruction Models for Large-scale Network Service Systems

Peer-to-Peer Networking and Applications > Ausgabe 2/2019
Zhaohui Zhang, Lina Ge, Pengwei Wang, Xinxin Zhou
Wichtige Hinweise
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


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.

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