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.
Highlights
In the recent years, large-scale service systems based on Internet have witnessed rapid evolution such as growth ofThis article is part of the Topical Collection: Special Issue on Software Defined Networking: Trends, Challenges and Prospective Smart Solutions Guest Editors: Ahmed E
The first set of simulated experimental data is applied to the load balancing algorithm of the system behavior reconstruction process; the experimental results are shown in Fig. 6, where the real-time load changes with time are illustrated
This paper proposes a system behavior reconstruction model based on the user interaction time sequence characteristics, with the aim of resolving the system overloading issue resulting from the rapid growth of user behaviors large-scale network service systems, by the way of delaying user behavioral time
Summary
Large-scale service systems based on Internet have witnessed rapid evolution such as growth of. Large-scale user concurrent processing system are usually affected whilst expanding resources, whereby risking overloading the system due to uncertain user behaviors after expanding the computing resources To this end, software self-adaptation strategies have been put forward to cope with this system overloading issue whilst expanding the system resources and to combat the complexities faced due to the increasing Internet service systems. It is important that special attention should be given to restructure the system behaviors in accordance with the changes in user behaviors by balancing the system load. The remainder of the paper is organized as follows: Section 2 reviews the related works and Section 3 presents the proposed system behavior reconstruction model.
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