An introduction to fuzzy control
An introduction to fuzzy control
Efficient fair queueing using deficit round-robin
IEEE/ACM Transactions on Networking (TON)
Design of a fuzzy traffic controller for ATM networks
IEEE/ACM Transactions on Networking (TON)
A GA paradigm for learning fuzzy rules
Fuzzy Sets and Systems - Special issue on connectionist and hybrid connectionist systems for approximate reasoning
ATM networks: principles and use
ATM networks: principles and use
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
ATM Networks: Concepts, Protocols, Applications
ATM Networks: Concepts, Protocols, Applications
Fair and Efficient Packet Scheduling Using Elastic Round Robin
IEEE Transactions on Parallel and Distributed Systems
ATM Cell Scheduling by Function Level Evolvable Hardware
ICES '96 Proceedings of the First International Conference on Evolvable Systems: From Biology to Hardware
Dynamic Control of Genetic Algorithms Using Fuzzy Logic Techniques
Proceedings of the 5th International Conference on Genetic Algorithms
Priority Scheduling and Buffer Management for ATM Traffic Shaping
FTDCS '99 Proceedings of the 7th IEEE Workshop on Future Trends of Distributed Computing Systems
Implementation of evolutionary fuzzy systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
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In packet switching network such as asynchronous transfer mode (ATM), the switching characteristics is important in delivering the guaranteed QoS (Quality of Service) level of the network. Many methods have been developed to control cell flow for shared bandwidth. The first-in first-out (FIFO), static priority (SPR), dynamically weighted priority scheduling (DWPS) (T. Lizambri, F. Duran, and S. Wakid, 1999) and weighted fair queuing (WFQ) (R. Händel, M.N. Huber, and S. Schröder, c1998) are some of the schemes for managing the shared bandwidth. Due to the diversity of services supported in ATM network, it is typical for the traffic flow pattern to change dramatically. A common trait of these algorithms is that their mechanisms are fixed, and they cannot adapt efficiently for such traffic flow changes. In order to address this, we propose an evolutionary fuzzy system (EFS) scheme to do ATM cell scheduling. With EFS, the fuzzy switching algorithm can be adjusted to track the changes in the pattern of traffic flow in order to maintain the desired level of performance. The desired quality of service (QoS) performance level can be conveniently achieved by tuning the parameters of the fitness function.