Hybrid neuro - fuzzy based adaptive load balancing for delay - sensitive internet application

Article type: Research Article

Authors: Chimmanee, Sanon | Wipusitwarakun, Komwut | Runggeratigul, Suwan

Affiliations: Information Technology, Department, Sirindhorn International Institute of Technology, Thammasat University, Thailand

Note: [] Corresponding author. E-mail: [email protected]

Abstract: Delay-sensitive Internet applications are required for high service quality in IP networks. Unfortunately, the existing load balancing tools are developed to distribute Internet traffic between routes without considering the current status of applications and network resources. This results in a degradation of quality of the applications when these load balancing tools are implemented. Since the characteristics of Internet applications such as traffic patterns are uncertain, and the usage of network resources is time-variant, an adaptive load-balancing tool is needed to cope with the changes in the current status of the system. In our previous work, we have presented a per-application load balancing for voice over IP based on a neuro-fuzzy integration. The previous concept of load balancing is to classify applications with similar characteristics to the same class and results in achieving the desired targets. This paper intends to extend upon the previous mechanism in order to apply it to other delay-sensitive applications. However, since the previous concept and mechanism are not applicable to general Internet applications, this paper proposes the idea of load balancing to classify applications with different characteristics to be possible in the same class provided that the QoS of the desired application is not degraded. This results in optimizing both the QoS requirement for the general delay-sensitive applications and the utilization of network resources, simultaneously.

Keywords: Intelligence adaptive control, hybrid neuro-fuzzy system, adaptive classification, load balancing, and Quality of Service (QoS)

Journal: Journal of Intelligent & Fuzzy Systems, vol. 16, no. 2, pp. 79-93, 2005