Random early detection gateways for congestion avoidance
IEEE/ACM Transactions on Networking (TON)
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
When the CRC and TCP checksum disagree
Proceedings of the conference on Applications, Technologies, Architectures, and Protocols for Computer Communication
Intelligent Control: Aspects of Fuzzy Logic and Neural Nets
Intelligent Control: Aspects of Fuzzy Logic and Neural Nets
Modeling TCP Throughpu: A simple model and its empirical validation
Modeling TCP Throughpu: A simple model and its empirical validation
A report on recent developments in TCP congestion control
IEEE Communications Magazine
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The Internet and most current intranet networks are experiencing a huge increase in the volume of traffic. This affects directly the network congestion by saturating the buffers at the routers and contributes to generating lots of data losses as well as reception and transmission delays. The existing TCP end-to-end congestion control uses Additive Increase Multiplicative Decrease (AIMD) approach, a time out and slow start behavior, which lead to data throughput with abrupt changes. Therefore, developing new congestion control strategies based on non-analytical approaches will certainly help to overcome the current difficulties of the internet in particular which are due to network structural complexity, diversity of services supported, and to variety of parameters involved. This work presents a fuzzy logic-based approach for controlling the network congestion. Its main objective is to optimize the available bandwidth and keep smooth the data throughput transfer profile.