Designing effective control strategies for asynchronous transfer mode (ATM) networks is known to be difficult because of the complexity of the structure of networks, nature of the services supported, and variety of dynamic parameters involved. Additionally, the uncertainties involved in identification of the network parameters cause analytical modeling of ATM networks to be almost impossible. This renders the application of classical control system design methods (which rely on the availability of these models) to the problem even harder. Consequently, a number of researchers are looking at alternative non-analytical control system design and modeling techniques that have the ability to cope with these difficulties to devise effective, robust ATM network management schemes. Those schemes employ artificial neural networks, fuzzy systems and design methods based on evolutionary computation. In this survey, the current state of ATM network management research employing these techniques as reported in the technical literature is summarized. The salient features of the methods employed are reviewed.

Computational intelligence in management of ATM networks

Future broadband networks like BISDN have to accommodate considerably diverse payload types on a common packet switched platform. The ATM is a fixed packet length transfer mode defined by the ITU-T as the preferred transfer standard for BISDN networks and is able to integrate different services efficiently. The problems associated with the characterisation of bursty and VBR (variable bit rate) ATM traffic has given rise to some concerns in the field of network management. While many conventional approaches like M/D/1 queues and the MMPP (Markov modulated Poisson process) do not yield totally acceptable results, self-adaptive fuzzy logic methods are particularly suitable for nonlinear and bursty environments like ATM systems. This paper presents a fuzzy logic CAC (connection admission control) and traffic policing method intended for the modelling and management of ATM networks.

Fuzzy adaptive traffic enforcement for ATM networks

Network management is a real-time observation and control action to optimize the grade of service of a network. More emphasis is being placed on the intelligent automatic exchange for stored program controlled (SPC) systems that are capable of real-time network management. The modeling of traffic characteristics and the prediction of future traffic flow are the first steps to efficient network control and management. This paper presents a fuzzy logic self-learning model and adaptive predictor of traffic now. The results of an application of this predictor on real world telephone links, are shown, and are compared to applications of a math-model and a Kalman-filter predictor.

http://ieeexplore.ieee.org/iel2/3865/11282/00512434.pdf

Fuzzy modeling and prediction of network traffic fluctuations

The research presented in this thesis aims to demonstrate that fuzzy logic is a useful tool for developing mechanisms for controlling traffic flows in ATM based multimedia networks to maintain quality of service (QoS) requirements and maximize resource utilization. The study first proposes a hierarchical, multilevel control structure for ATM networks to exploit the reported strengths of fuzzy logic at various control levels. Then, an extensive development and evaluation is presented for a subset of the proposed control architecture at the congestion control level. An ATM based multimedia network must have quite sophisticated traffic control capabilities to effectively handle the requirements of a dynamically varying mixture of voice, video and data services while meeting the required levels of performance. Feedback control techniques have an essential role for the effective and efficient management of the resources of ATM networks. However, development of conventional feedback control techniques relies on the availability of analytical system models. The characteristics of ATM networks and the complexity of service requirements cause the analytical modeling to be very difficult, if not impossible. The lack of realistic dynamic explicit models leads to substantial problems in developing control solutions for B-ISDN networks. This limits the ability of conventional techniques to directly address the control objectives for ATM networks. In the literature, several connection admission and congestion control methods for B-ISDN networks have been reported, and these have achieved mixed success. Usually they either assume heavily simplified models, or they are too complicated to implement, mainly derived using probabilistic (steady-state) models. Fuzzy logic controllers, on the other hand, have been applied successfully to the task of controlling systems for which analytical models are not easily obtainable. Fuzzy logic control is a knowledge-based control strategy that can be utilized when an explicit model of a system is not available or, the model itself, if available, is highly complex and nonlinear. In this case, the problem of control system design is based on qualitative and/or empirically acquired knowledge regarding the operation of the system. Representation of qualitative or empirically acquired knowledge in a fuzzy logic controller is achieved by linguistic expressions in the form of fuzzy relational equations. By using fuzzy relational equations, classifications related to system parameters can be derived without explicit description. The thesis presents a new predictive congestion control scheme, Fuzzy Explicit Rate Marking (FERM), which aims to avoid congestion, and by doing so

M.F. Scheffer

Papers

VLSI Hardware Realization of Self-Learning Recursive Fuzzy Model for Dynamic Systems

Application Using VLSI Hardware Realization of Self–Learning Recursive Fuzzy Model