The Impact of Jitter on Traffic Flow Optimization in Communication Networks

Dahmouni, Hamza; Girard, André; Ouzineb, Mohammed; Sansò, Brunilde

doi:10.1109/tnsm.2012.051712.110148

Cited by 23 publications

(9 citation statements)

References 30 publications

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“…In (Baldi and Marchetto [29], Baldi et al [30]), a packet scheduling technique, time-driven priority with pipeline forwarding, was applied to create a multimedia delivery framework providing optimized real-time video transmission in terms of both perceptual quality and resource utilization. The authors in (Dahmouni et al [31]) provided an analytical jitter model for routing optimization in order to examine the effect of jitter constraints on network performance. The simulation results showed that the jitter-constrained flows experienced low-path switching.…”

Section: Routing For Real-time Trafficmentioning

confidence: 99%

The Random Neural Network for Cognitive Traffic Routing and Task Allocation in Networks and the Cloud

Wang

2017

Prob. Eng. Inf. Sci.

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G-Network queueing network models, and in particular the random neural network (RNN), are useful tools for decision making in complex systems, due to their ability to learn from measurements in real time, and in turn provide real-time decisions regarding resource and task allocation. In particular, the RNN has led to the design of the cognitive packet network (CPN) decision tool for the routing of packets in the Internet, and for task allocation in the Cloud. Thus in this paper, we present recent research on how to dynamically create the means for quality of service (QoS) to end users of the Internet and in the Cloud. The approach is based on adapting the decisions so as to benefit users as the conditions in the Internet and in Cloud servers vary due to changing traffic and workload. We present an overview of the algorithms that were designed based on the RNN, and also detail the experimental results that were obtained in three areas: (i) traffic routing for real-time applications, which have strict QoS constraints; (ii) routing approaches, which operate at the overlay level without affecting the Internet infrastructure; and (iii) the routing of tasks across servers in the Cloud through the Internet.

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Section: Routing For Real-time Trafficmentioning

confidence: 99%

The Random Neural Network for Cognitive Traffic Routing and Task Allocation in Networks and the Cloud

Wang

2017

Prob. Eng. Inf. Sci.

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“…The main result is that delay and jitter behave in different manners. Also, a model for optimizing the routing of Poisson traffic was proposed in [6]. This model is formulated as a function describing the trade-off between the delay and jitter.…”

Section: Routing Optimization For Jittermentioning

confidence: 99%

An efficient algorithm for traffic flow optimization in MPLS networks

Mesbahi

Dahmouni

2015

2015 International Conference on Protocol Engineering (ICPE) and International Conference on New Technologies of Distributed Sy

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The topic of this paper is to propose a new network design algorithm which defines an efficient multi-path routing scheme in an MLPS network. Our goal is to offer QoS guarantees for delay and jitter which are considered as important performance metrics for interactive and real time services. To this end, we have formulated several nonlinear objective functions for determining network link utilization as well as traffic flow routing through the network. Several issues are described and simulations are performed in order to evaluate our model. Furthermore, we have proposed three load balancing modules: one which minimizes the network link utilization and offers performance guarantee in terms of delay. The second model aims to select feasible paths by taking into account the characteristics and behavior of the jitter by defining the splitting scheme. The third one is a general model based on the two previous algorithms which defines the routing scheme for traffic sensitive to delay and jitter. Finally, our model is compared to other multi-path routing algorithms, and the results showed a significant improvement of QoS parameters and network link utilization than these models.

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“…The escalating demands in performance require improved resource management, yet Transport Control Protocol (TCP) based to satisfy new users within existing network resources. Then, AQM arose as an effective way to tune TCP behavior, by discarding selectively packets to smoothly regulate the traffic rate and delay variation, called jitter, which threatens nowadays the multimedia network services (Abharian et al 2012;Dahmouni et al 2012;El Fezazi et al 2019a). AQM mechanisms are introduced to assist the TCP/IP congestion control for its relation with Internet traffic congestion and Quality of Service (QoS) demands of users and applications (Alaoui et al 2018(Alaoui et al , 2019Bigdeli and Haeri 2007;Lamrabet et al 2017).…”

Section: Introductionmentioning

confidence: 99%

AQM Congestion Controller for TCP/IP Networks: Multiclass Traffic

Fezazi

Elfakir

Bender

et al. 2020

J Control Autom Electr Syst

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Active queue management (AQM) is a congestion control mechanism for the early notification of the incipient congestion pursued by dropping/marking packets. The novelty in our result is that we are able to consider heterogeneous traffic (i.e., multiclass traffic) where each class has its own packet discarding policy, queue size, and bandwidth share. Then, this is so far the first control theory-based approach for the AQM problem on the TCP/IP routers that allows multiclass AQM. Our proposed technique assumes that each class has already a controller, designed a priori, and focuses on designing a static state feedback controller where the proposed design approach is based on the solution of linear matrix inequalities (LMIs). From the theoretical conditions, a new controller design methodology for discrete time systems with multi-delays, saturated inputs, and disturbances is proposed to overcome the obstacle of bilinearity which leads to a quite simple LMI condition that is numerically tractable with any convex optimization algorithm. A numerical example is provided at the end of this paper to show the effectiveness and performances of the proposed approach in the presence of multiclass traffic.

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The Impact of Jitter on Traffic Flow Optimization in Communication Networks

Cited by 23 publications

References 30 publications

The Random Neural Network for Cognitive Traffic Routing and Task Allocation in Networks and the Cloud

The Random Neural Network for Cognitive Traffic Routing and Task Allocation in Networks and the Cloud

An efficient algorithm for traffic flow optimization in MPLS networks

AQM Congestion Controller for TCP/IP Networks: Multiclass Traffic

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