Network Planning and Adaptive Routing for Multimedia Traffic - UnB

Network Planning and Adaptive Routing for MultimediaTrafficPriscila Solís Barreto 1 , Paulo H. P. de Carvalho 2 , Rafael Dias Oliveira 3and Maximiliano Prestes Ceppo 32 Department of Computer Science, 1 Department of Electrical EngineeringUniversity of Brasilia, Brasília-DF, Brasil2 pris@cic.unb.br, 1 paulo@ene.unb.br, 3 {rafael,max}@labcom.unb.brAbstract. . This work presents a methodology for network traffic engineering based ona hybrid traffic model and an adaptive routing algorithm. The hybrid traffic modelpursuits the calculation of more accurate Qos metrics and effective bandwidth formultimedia traffic in order to optimize the sizing of network elements at a minimalcost. During network operation, the links weighted values are modified to computeshortest path routing algorithms in an adaptive manner to comply with the trafficeffective bandwidth and QoS requirements. The network routing methodology wasevaluated in a network topology that aggregates different traffic types, Poisson and selfsimilar.The results show that the proposed methodology achieves the QoSrequirements and promotes a more efficient traffic balancing within the network.Keywords. traffic characterization, adaptive routing, QoS metrics, optimization.1. IntroductionThe multimedia traffic results when integrating, over a single communicationchannel, a diverse range of traffic sources [1,2]. Different observations of operatingnetworks confirm that the aggregated traffic in the cores, i.e. the multimedia traffic,has a self-similar and multifractal nature [3,4,5,6,7,8,9] which results frommultiplexing different sources, that significantly differ in their traffic finite buffersystems when compared to the Markovian traffic sources.Also, observations show that the existence of noticeable concentrated periods ofcontention and idleness of self-similar traffic can greatly increase packet delay andloss probability and thus reduce system resource utilization [11]. Also, severalresearch works point that nowadays the research problems and challenges for networktraffic may be grouped into three areas: workload characterization, performanceanalysis and traffic control [10].Some models have been developed to treat traffic characterization of self-similarand multimedia traffic [11,12,13]. Some of these works appear to have a certaintendency to overestimate the QoS values while others rely on the extraction of morethan three traffic parameters to perform the characterization. Clearly, thisoverestimation has a negative impact when trying to optimize network planning.

Network PlanningTrafficMatrixCharacterizationSizingOptimizationQoS and CostRequirementsHybrid TrafficModelNetworkTopologyRoutingModelPerformanceEvaluationNewFlowsAdmissionControlMetricsyesOK?noRoutingOptimizationNetwork OperationFig. 1. Traffic Engineering FrameworkAs can be seen in Figure 1, the Control Admission procedure relies as well on thePerformance Evaluation procedure. If the QoS requirements for that flow can not beachieved, the Routing Optimization procedure is triggered and the network routingmodel is updated. The variables to implement the methodology are summarized inTable 1.Table 1. Variables for Traffic engineering with traffic characterizationGivenMinimizeWith thevariablesRestrictionsA network topologyA traffic matrix R HThe cost function for link capacitiesd i (C i )A set of traffic trunks defined byFECs=FA cost functionVP R =(L 1 ,L 2 , ....,L b )C=(C 1 , C 2 ,...C n )F L ={(f,l) ∈ M:FVP R }( f , L)< ρ(L)∑ii∀fi∈ ( fi,L),QoSM ( fi,L)≤ QoSR(fi),L∈ VP R and exists a f l ∈ F where f i =f lIn Table 1, the QoSR function defines the QoS requirements of a specific serviceclass and the QoSM function defines the QoS values of that traffic class when mappedin a VP (virtual path). The QoS parameters are the delay and packet loss probability.The ρ(L) function defines the maximum admissible load for the link L. The set oftrunks F are the traffic flows grouped by FEC (Forwarding Equivalence Classes)which are mapped in virtual paths when using the function M.Each flow f n is characterized by a group (S,A,P) in which S=(H,a,σ 2 ), with H as theHurst parameter for the traffic trace, a is the mean rate and e σ 2 is the variance. Theparameters A and P are the maximum delay and the maximum loss admissible for theflow, calculated with function QoSR.

The above definitions induce that, for a flow to be associated to a FEC, it shouldcomply the following conditions:(i) ∀ f i ∈ T m ∈ FEC s , Ag H (H i ,T m )

) For a traffic load in the network links lower than 30%, independently of theH value or when the H value is lower than 0.5, the QoS values are calculatedwith the M/M/1/k model, in which k denotes the buffer size.c) For link loads between 30% and 100%, the QoS values are calculated with adeterministic fBm envelope divided by the channel capacity c, as proposedin 13.d) For packet loss probability, when it occurs in a node, the traffic flow thatarrives to the following node in the virtual path suffers an influence of thisloss since the H value depends on the value of the aggregated variance. Wepropose the calculus of the media value by preserving the value of thevariance coefficient, which is the relation between the mean value andvariance.Within the traffic engineering methodology, the routing strategy defines anadaptive dynamic based on effective bandwidth estimation. The effective bandwidth[21,22] definition for self-similar flows when characterized as fBm flows [12,13] isshown in (1), with m as the mean, K(H)=H H (1-H) H , a is the variance coefficient, H isthe Hurst parameter and P loss is the loss probability with a buffer size b.1 −(1−H ) 11/ H2HH 2HEB = m + K ( H ) − 2 ln Plossa b m(1)Cost calculation Cost calculation Cost calculationEffectiveBandwidthEffectiveBandwidthEffectiveBandwidthInterface 1 Interface 2 Interface N...OSPFSource trafficFig. 2. Adaptive Routing SchemeIn Figure 2 the adaptive routing procedure is described. While the packets flow inthe different node interfaces, the number of packets during the time interval s as wellas their packet sizes are kept by the effective bandwidth block. In this block, amonitor that tracks the link occupancy returns the link load. Based on the definition ofthe hybrid traffic model described above the values of effective bandwidth arecalculated.After the effective bandwidth calculus, its value is parameter to the OSPF module,which modifies the link cost to the relation (effective bandwidth/link capacity). Thealteration is done simultaneously in all the interfaces. After this alteration, thecommon dynamics of the OSPF algorithm modify the network paths within thenetwork.

Fig. 3. Experimental Network TopologyTable 2. Traffic flows parametersFlow Source Destiny First H Mean Rate (kBps) VarianceID Node Node Window(kBps)1 1 8 1 0.75 12.5 283542 2 8 11 0.5 8 163753 4 3 1 0.5 10 249894 4 8 11 0.85 7.5 196485 6 3 11 0.60 7.5 92196 7 3 1 0.80 8 48406The results of delay and packet losses for flow 3 are shown in Figure 6 and Figure7 respectively. For both cases, the adaptive routing procedure reduced all the QoSmetrics for the flow. In the case of packet loss the reduction is very significantly. Thesame observation appears for the flow 6 delay value in Figure 8. For this flow thepacket losses were 0 for both cases. The mean jitter for flow 6 appears to have alower value for the adaptive routing algorithm, as shown in Figure 9.In Table 3 are shown the comparative results for mean delay, mean jitter andnumber of packet losses for all the flows, for the adaptive and inverse weighted linkcost. Since the jitter has not been used a restriction metric in our model, its results areused just as a reference variable for the other QoS metrics.As can be seen in Table 3,the reduction of packet loss has the most significantly reduction for almost all thecases when applied the adaptive routing algorithm. The results are always lower thanthe ones for the traditional inverse cost calculus. The mean delay for some flowsincrease with the adaptive routing algorithm, but as long as it remains under the QoSlimit value, the result may be considered acceptable.Even when the jitter was not a restriction variable considering in the adaptiverouting process, it can be observed in Table 3 that for most of the traffic flows, thejitter was also reduced when applying the proposed methodology.

1.61.4AdaptiveInverseDelay6050AdaptiveInversePacket Loss1.2140Value (s)0.8Value300.6200.40.21000 2 4 6 8 10 12 14 16 18 20Window00 2 4 6 8 10 12 14 16 18 20WindowFig. 4. Delay for flow 1Fig. 5. Packet losses for flow 19080AdaptiveInversePacket Loss1.41.2AdaptiveInverseDelay70601Value5040Value (s)0.80.6300.420100.200 2 4 6 8 10 12 14 16 18 20WindowFig. 6. Packet losses for flow 300 2 4 6 8 10 12 14 16 18 20WindowFig. 7. Delay for flow 30.0560.054AdaptiveInverseDelay0.01260.01240.0122AdaptiveInverseJitter0.0520.012Value (s)0.05Value (s)0.01180.01160.0480.01140.0460.01120.0110.0440 2 4 6 8 10 12 14 16 18 20WindowFig. 8. Delay for flow 60.01080 2 4 6 8 10 12 14 16 18 20WindowFig. 9. Jitter for flow 6

Table 3. Comparative Traffic QoS ResultsMean Delay(seconds)Mean Jitter(seconds)Packet Losses(no. of packets)Flow Inv Adap Inv Adap Inv Adap1 0.5846 0.7625 0.0892 0.0329 510 752 0.0489 0.1334 0.0106 0.0121 0 03 0.5353 0.1028 0.1078 0.0116 900 04 0.9922 1.3094 0.1861 0.0859 740 1745 1.0291 0.0716 0.0816 0.0104 240 06 0.0466 0.0445 0.0114 0.0106 0 04. ConclusionsThis article proposed a methodology for network planning and adaptive routing formultimedia traffic. The methodology relies on a hybrid traffic model for multimediatraffic. The traffic model characterizes the traffic to produce more accurate QoSmetrics that may be used to optimize the cost of network elements in order to reducethe network planning cost. Within the proposed methodology, the routingoptimization procedure enters in operation when the control admission proceduredetects the impossibility to map a new traffic flow within the established virtual paths.The routing optimization procedure uses an adaptive routing strategy. The adaptiverouting strategy modifies the weighted links costs in a dynamic approach based on theeffective bandwidth necessity. The calculus of the effective bandwidth relies in thehybrid traffic model and the OSPF algorithm is used to compute the network paths.The proposed methodology is evaluated in an optimized network with a preestablishedtraffic matrix and a pre-defined routing strategy. The results show thatwhen the network is in operation, the adaptive updating of weighted links costs whencompared to the static approach, results in a higher degree of load balancing whichimproves the maintenance of the traffic QoS requirements for almost all the traffics.The results were specially improved for the packet loss values. The future work ofthis research will evaluate the proposed methodology for multifractal traffic.5. References1. Rosen E., Viswanathan A., Callon R. Request for Comments: 3031. Janeiro 2001.2. Evans J., Filsfils C. Deploying IP and MPLS QoS for multiservice networks. ISBN-13:978-0-12-370549-5, Morgan Kaufmann, 2007.3. W. E. Leland, M. S. Taquu, W. Willenger and D. V. Wilson. On the self- similar nature ofethernet traffic (extended version). In: IEEE/ACM Trans. on Networking, vol. 2, n. 1, pp.1-15, 1994.4. Park K., Willinger W. Self-Similar Network Traffic and Performance Evaluation. JohnWiley and sons. ISBN: 9780471206446, 2000.5. Sahinoglu, Z. Tekinay, S, On multimedia networks: self-similar traffic and networkperformance. In: IEEE Communications, Volume: 37, Issue: 1, pp. 48-52, 1999.

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