A Robust Distributed Congestion-Control Strategy for Differentiated-Services Network

Bouyoucef, K.; Khorasani, K.

doi:10.1109/tie.2008.2010080

Cited by 19 publications

(11 citation statements)

References 38 publications

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“…The three edge nodes 1, 2 and 3 can communicate with each other to share the information among the three clusters. This network configuration is quite general and can be found in many applications such as sensor/actuator networks, cooperative team of unmanned vehicles [15], [16], [17], [18], and high speed Ethernet networks. For our simulation studies we implement the network behavior by an event-based simulator tool known as QualNet [25] software environment.…”

Section: Performance Evaluations and Simulation Resultsmentioning

confidence: 99%

“…Consequently, the presence of unknown and time-varying delays and latencies are not considered in the design of the congestion control scheme. The lack of explicit consideration of the delays will yield a critical challenge and even an instability when the approach is applied to a large scale network consisting of many nodes structured in arbitrary configurations containing feedback [15], [16], [17], [18].…”

Section: Introductionmentioning

confidence: 99%

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A robust adaptive congestion control strategy for large scale networks with differentiated services traffic

Chen

Khorasani

2011

Automatica

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Abstract-In this paper, a robust decentralized congestion control strategy is developed for a large scale network with Differentiated Services (Diff-Serv) traffic. The network is modeled by a nonlinear fluid flow model corresponding to two classes of traffic, namely the premium traffic and the ordinary traffic. The proposed congestion controller does take into account the associated physical network resource limitations and is shown to be robust to the unknown and time-varying delays. Our proposed decentralized congestion control strategy is developed on the basis of Diff-Serv architecture by utilizing a robust adaptive technique. A Linear Matrix Inequality (LMI) condition is obtained to guarantee the ultimate boundedness of the closed-loop system. Numerical simulation implementations are presented by utilizing the QualNet and Matlab software tools to illustrate the effectiveness and capabilities of our proposed decentralized congestion control strategy.

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Section: Performance Evaluations and Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A robust adaptive congestion control strategy for large scale networks with differentiated services traffic

Chen

Khorasani

2011

Automatica

Self Cite

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“…Other studies proposed modifications in the Random Early Detection (RED) algorithm to self-adjust its parameters based on the estimation of future state and flow priority [11], [12]. In spite of the simplicity of the proposal, congestion situations are not properly solved in the core, as resources are used by the traffic that eventually is discarded, instead of being dedicated to more effective tasks.…”

Section: A Congestion Control Techniquesmentioning

confidence: 99%

Profile-Based Adaptive DiffServ Policing with Learning Techniques

Cruvinel

Vazão

2011

2011 Proceedings of 20th International Conference on Computer Communications and Networks (ICCCN)

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IETF's Differentiated Services (DiffServ) is a scalable, distributed architecture aimed to provide a fair distribution of network resources according to expected levels of service. However, variable traffic and performance expectations may require an adjustment of management policies so that congestion situations are avoided and service parameters are met. Existing solutions that improve the standard DiffServ architecture do not cope well with this dynamics and network resources are not efficiently managed, preventing user expectations from being properly fulfilled. In this paper we show how a reinforcement learning approach can optimize the choice of adaptation profiles for the optimal adjustment of ingress policies when a precongestion state is detected. Numerical results from simulations showed improvements for priority traffic (video) without impacting excessively the other traffic.

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“…By using the RISE design approach, a continuous congestion control strategy is developed in this paper using only the error measurements between the actual and the desired ensemble average queue length for Premium Traffic Service for DiffServ networks. This approach is different from [20] in the sense that the controller is continuous, and global asymptotic stability of the ensemble average queue length in the Premium Service buffer is obtained.…”

mentioning

confidence: 98%

“…An ultimately bounded stability result is obtained by assuming that the derivative of the ensemble average arrival rate is bounded by a finite constant. A sliding mode variable structure congestion controller was utilized in [20] based on the FFM. In [21], a second order sliding mode controller was introduced that claimed that the ensemble average arrival rate for premium service was unknown while using the same in the controller.…”

mentioning

confidence: 99%

Continuous congestion control for Differentiated-Services networks?

Subramanian

Curtis

Pasiliao

et al. 2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

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Network traffic in the transport layer of endto-end congestion networks plays a vital role in effecting the throughput in the Medium Access Control (MAC) layer. Common queue length management techniques on nodes in such networks focus on servicing the packets based on their Quality of Service (QoS) requirements (e.g., DifferentiatedServices, or DiffServ, networks). In this paper, a continuous control strategy is suggested for a DiffServ network to track the desired ensemble average queue length level specified by the network operator. A Lyapunov-based stability analysis is provided to illustrate global asymptotic tracking, and simulations demonstrate the performance and feasibility of the controller, along with showing global asymptotic tracking of the queue lengths in the Premium Service buffer.

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A Robust Distributed Congestion-Control Strategy for Differentiated-Services Network

Cited by 19 publications

References 38 publications

A robust adaptive congestion control strategy for large scale networks with differentiated services traffic

A robust adaptive congestion control strategy for large scale networks with differentiated services traffic

Profile-Based Adaptive DiffServ Policing with Learning Techniques

Continuous congestion control for Differentiated-Services networks?

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