Fuzzy explicit marking: A unified congestion controller for Best-Effort and Diff-Serv networks

Chrysostomou, Chrysostomos; Pitsillides, Andreas; Şekercioğlu, Y. Ahmet

doi:10.1016/j.comnet.2008.11.002

Cited by 40 publications

(21 citation statements)

References 22 publications

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“…This development is motivated by the difficulties experienced when modeling communication networks by using conventional analytical methods. Some of the fuzzy applications include power control [23] in cellular systems; congestion control in IP networks [26], [85]; routing [5] and data fusion [62] in wireless sensor networks; and Quality of Service management in wireless sensor and actuator networks [125]. Input parameters are, generally, sampled at a fixed rate and the fuzzy system is triggered accordingly.…”

Section: Fuzzy Logic Based Applications In Communication Networkmentioning

confidence: 99%

Fuzzy Logic Is Not Fuzzy: World-renowned Computer Scientist Lotfi A. Zadeh

Dzițac¹,

Filip²,

Manolescu³

2017

INT J COMPUT COMMUN

114

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Section: Fuzzy Logic Based Applications In Communication Networkmentioning

confidence: 99%

Fuzzy Logic Is Not Fuzzy: World-renowned Computer Scientist Lotfi A. Zadeh

Dzițac¹,

Filip²,

Manolescu³

2017

INT J COMPUT COMMUN

114

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“…The second phase -fuzzy avoidance-performs a fuzzy decision-making approach to proactively respond to network congestion rather than simply wait for a congested queue to overflow and the tail drop all subsequently arriving data packets. The application of fuzzy decision-making techniques to the problem of congestion control is suitable due to the difficulties in obtaining a precise mathematical (or a formal analytical) model, while some intuitive understanding of congestion control is available [39,40]. Its use allows to regulate effectively the incoming Interest packets in each routers' interface.…”

Section: Introductionmentioning

confidence: 99%

ACCPndn: Adaptive Congestion Control Protocol in Named Data Networking by learning capacities using optimized Time-Lagged Feedforward Neural Network

Karami

2015

Journal of Network and Computer Applications

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Named Data Networking (NDN) is a promising network architecture being considered as a possible replacement for the current IP-based Internet infrastructure. However, NDN is subject to congestion when the number of data packets that reach one or various routers in a certain period of time is so high than its queue gets overflowed. To address this problem many congestion control protocols have been proposed in literature which, however, they are highly sensitive to their control parameters as well as unable to predict congestion traffic well enough in advance. This paper develops an Adaptive Congestion Control Protocol in NDN (ACCPndn) by learning capacities in two phases to control congestion traffics before they start impacting the network performance. In the first phase -adaptive trainingwe propose a Time-Lagged Feedforward Network (TLFN) optimized by hybridization of particle swarm optimization and genetic algorithm to predict the source of congestion together with the amount of congestion. In the second phase -fuzzy avoidance-we employ a non-linear fuzzy logic-based control system to make a proactive decision based on the outcomes of first phase in each router per interface to control and/or prevent packet drop well enough in advance. Extensive simulations and results show that ACCPndn sufficiently satisfies the applied performance metrics and outperforms two previous proposals such as NACK and HoBHIS in terms of the minimal packet drop and highutilization (retrying alternative paths) in bottleneck links to mitigate congestion traffics.

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“…This development is motivated [1] by the difficulties experienced when modeling communication networks by using conventional analytical methods. Some of the fuzzy applications include power control [2] in cellular systems; congestion control in IP netwoks [3], [4]; routing [5] and data fusion [6] in wireless sensor networks; and Quality of Service management in wireless sensor and actuator networks [7]. Input parameters are, generally, sampled at a fixed rate and the fuzzy system is triggered accordingly.…”

Section: Introductionmentioning

confidence: 99%

“…In FLCD+DS, the sampling period τ is initialized randomly within (0,1] seconds. Based on the existing congestion control literature [3], τ = 2 msec in FLCD.…”

Section: Experiments 2 -Efficiency Of the Dynamic Sampling Rate Mechanismmentioning

confidence: 99%

Distance Based Triggering and Dynamic Sampling Rate Estimation for Fuzzy Systems in Communication Networks

Nyirenda

Dong

Hirota

2011

INT J COMPUT COMMUN

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To reduce computational cost in fuzzy systems in communication networks, distance based triggering and sampling rate adaptation probabilities are proposed based on the concept of probability via expectation. The triggering probability, which is calculated by using the square of distance between subsequent input vectors, governs the rate at which the fuzzy system is triggered. The dynamic sampling rate probability, which governs the adaptation of the sampling rate, is computed by using the exponentially weighted moving average (EWMA) of the triggering probability. A stopping criterion, based on convergence tests, is also proposed to ensure that the mechanism switches off when the sampling period has converged. The triggering mechanism reduces the number of computations in the Fuzzy Logic Congestion Detection (FLCD) in wireless Local Area Networks (WLANs) by more than 45%. Performance, in terms of packet loss rate, delay, jitter, and throughput, however, remains virtually the same. On the other hand, the dynamic sampling rate mechanism leads to more than 150% improvement in sampling rate and more than 70% reduction in fuzzy computations while performance in the other key metrics remains virtually the same. As part of future work, the proposed mechanism will be tested in fuzzy systems in wireless sensor/actuator networks.

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Fuzzy explicit marking: A unified congestion controller for Best-Effort and Diff-Serv networks

Cited by 40 publications

References 22 publications

Fuzzy Logic Is Not Fuzzy: World-renowned Computer Scientist Lotfi A. Zadeh

Fuzzy Logic Is Not Fuzzy: World-renowned Computer Scientist Lotfi A. Zadeh

ACCPndn: Adaptive Congestion Control Protocol in Named Data Networking by learning capacities using optimized Time-Lagged Feedforward Neural Network

Distance Based Triggering and Dynamic Sampling Rate Estimation for Fuzzy Systems in Communication Networks

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