Application of Computational Intelligence (Fuzzy Logic, Neural Networks and Evolutionary Programming) to Active Networking Technology

Abstract

Abstract. Computational intelligent techniques, e.g., neural networks, fuzzy systems, neuro-fuzzy systems, and evolutionary algorithms have been successfully applied for many engineering problems. These methods have been used for solving control problems in packet switching network architectures. The introduction of active networking adds a high degree of flexibility in customizing the network infrastructure and introduces new functionality. Therefore, there is a clear need for investigating both the applicability of computational intelligence techniques in this new networking environment, as well as the provisions of active networking technology that computational intelligence techniques can exploit for improved operation. We report on the characteristics of these technologies, their synergy and on outline recent efforts in the design of a computational intelligence toolkit and its application to routing on a novel active networking environment. IntroductionThe events in the area of computer networks during the last few years reveal a significant trend toward open architecture nodes, the behavior of which can easily be controlled. This trend has been identified by several developments [1,2] such as:• Emerging technologies and applications that demand advanced computations and perform complex operations • Sophisticated protocols that demand access to network Resources • Research toward open architecture nodes Active Networks (AN), a technology that allows flexible and programmable open nodes, has proven to be a promising candidate to satisfy these needs. AN is a relatively new concept, emerged from the broad DARPA community in 1994-95 [1,3,4]. In AN, programs can be "injected" into devices, making them active in the sense that their behavior and the way they handle data can be dynamically controlled and customized. Active devices no longer simply forward packets from point to point; instead, data is manipulated by the programs installed in the active nodes (devices). Packets may be classified and served on a per-application or per-user basis. Complex tasks and computations may be performed on the packets according to the content of [5][6][7][8][9][10]. CI is the study of the design of intelligent agents. An agent is something that acts in an environment-it does something. Agents include worms, dogs, thermostats, airplanes, humans, organizations, and society. An intelligent agent is a system that acts intelligently: What it does is appropriate for its circumstances and its goal, it is flexible to changing environments and changing goals, it learns from experience, and it makes appropriate choices given perceptual limitations and finite computation. The central scientific goal of computational intelligence is to understand the principles that make intelligent behavior possible, in natural or artificial systems. The main hypothesis is that reasoning is computation. The central engineering goal is to specify methods for the design of useful, intelligent artifacts. There are some concepts of CI like: fuzzy sets and ...