TY - GEN
T1 - Fuzzy-logic-based TCP congestion control system
AU - Al-Naamany, A. M.
AU - Bourdoucen, H.
PY - 2003
Y1 - 2003
N2 - 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.
AB - 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.
KW - Bandwidth optimization
KW - Congestion control
KW - Fuzzy logic
KW - TCP
UR - http://www.scopus.com/inward/record.url?scp=84904362048&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84904362048&partnerID=8YFLogxK
U2 - 10.1007/978-0-387-35703-4_13
DO - 10.1007/978-0-387-35703-4_13
M3 - Conference contribution
AN - SCOPUS:84904362048
SN - 9781475759501
T3 - IFIP Advances in Information and Communication Technology
SP - 180
EP - 190
BT - Network Control and Engineering for QoS, Security and Mobility II - IFIP TC6 / WG6.2 and WG6.7 2nd International Conf. on Network Control and Engineering for QoS, Security and Mobility, Net-Con 2003
PB - Springer New York LLC
T2 - IFIP TC6 / WG6.2 and WG6.7 2nd International Conference on Network Control and Engineering for QoS, Security and Mobility, Net-Con 2003
Y2 - 13 October 2003 through 15 October 2003
ER -