This paper is sponsored by the Ministry of Education and Research of the Republic of
Bulgaria in the framework of project No 105 “Multimedia Packet Switching Networks Planning with Quality of Service and Traffic Management”.This paper presents Quality of Service analyses in wired and
wireless IP networks based on the three popular techniques – RSVP, IntServ,
and DiffServ. The analyses are based on a quick approximation schema of
the traffic system with static and dynamic changes of the system bounds.
We offer a simulation approach where a typical leaky bucket model is ap-
proximated with a G/D/1/k traffic system with flexible bounds in waiting
time, loss and priority. The approach is applied for two cascaded leaky
buckets. The derived traffic system is programmed in C++. The simula-
tion model is flexible to the dynamic traffic changes and priorities. Student
criterion is applied in the simulation program to prove results. The results
of the simulation demonstrate the viability of the proposed solution and its
applicability for fast system reconfiguration in dynamic environmental circumstances. The simulated services cover a typical range of types of traffic
sources like VoIP, LAN emulation and transaction exchange

Atamian, Dimitar

Goleva, Mariya

Goleva, Rossitza

Nikolov, Tashko

Bulgarian Digital Mathematics Library at IMI-BAS

Serdica J. Computing 2 (2008), 45–56QUALITY OF SERVICE SYSTEM APPROXIMATION IN IPNETWORKS*Rossitza Goleva, Mariya Goleva, Dimitar Atamian, Tashko Nikolov,Kostadin GolevAbstract. This paper presents Quality of Service analyses in wired andwireless IP networks based on the three popular techniques – RSVP, IntServ,and DiffServ. The analyses are based on a quick approximation schema ofthe traffic system with static and dynamic changes of the system bounds.We offer a simulation approach where a typical leaky bucket model is ap-proximated with a G/D/1/k traffic system with flexible bounds in waitingtime, loss and priority. The approach is applied for two cascaded leakybuckets. The derived traffic system is programmed in C++. The simula-tion model is flexible to the dynamic traffic changes and priorities. Studentcriterion is applied in the simulation program to prove results. The resultsof the simulation demonstrate the viability of the proposed solution and itsapplicability for fast system reconfiguration in dynamic environmental cir-cumstances. The simulated services cover a typical range of types of trafficsources like VoIP, LAN emulation and transaction exchange.ACM Computing Classification System (1998): C.4, C., C.2.Key words: Packet network, IP, Quality of Service, VoIP, shaping.*This paper is sponsored by the Ministry of Education and Research of the Republic ofBulgaria in the framework of project No 105 “Multimedia Packet Switching Networks Planningwith Quality of Service and Traffic Management”.46 R. Goleva, M. Goleva, D. Atamian, T. Nikolov, K. Golev1. Introduction. IP networks and their high demand of Quality of Ser-vice (QoS) support is a challenging area for investigation. Recently the greaterpart of people’s working time depends on the network and storage capabilities.There are many papers that try to deal with QoS in IP networks. In this paper weanalyse the traffic policing and shaping effect of the three most used techniques –IntServ, DiffServ, RSVP and highlight the way it can be done for Network Sig-nalling Protocol (NSIS). The analyses are made on the basis of the three popularservices – VoIP, LAN emulation, transaction exchange [7, 8, 14, 16]. The trafficshaping effect is estimated with application of priorities and under observationof delay and delay jitter. The model uses queues and priorities specific for theIntServ, DiffSerf, RSVP.Dynamic capacity requirements and environment quality parameters areestimated for video and audio traffic sources in [1]. The sensitivity of the qualityparameters towards delay and delay jitter is demonstrated for wire and wire-less networks. The author conclusion is that the end-to-end quality cannot beguaranteed with DiffServ technique. In [3] multimedia services and their QoS re-quirements are investigated for a WiFi network. In [4] authors demonstrate calladmission control procedures for WiFi in order to guarantee QoS. A comparisonbetween IntServ and DiffServ can be seen in [2]. A traffic model with cascadedqueues suitable for core networks is shown in [12]. An analytical approach toqueueing behavior of IP buffers is demonstrated in [13]. The specific queue be-havior in WiMAX access channel is shown in [15]. A WLAN Brady voice trafficmodel is demonstrated in [17].2. Traffic sources. Voice over IP, LAN emulation and transactionexchange types of traffic sources are assumed in an example local area network.Some adjustments are made for every traffic type. In a Voice over IP (VoIP)service silence and talk intervals are exponentially distributed with equal meanvalues [7] with activity detection. There are authors who use a talk to silenceratio of 1/2. Others prefer to use an on-off model for voice service [14, 17]. Thelimits for waiting times are calculated under consideration of end-to-end delaylimits for every service [11]. The same is valid for queue length [10]. Servicingtimes per packets are fixed according to the assumed fixed length packet header.Table 1 represents all the traffic source parameters in the model.Quality of service system approximation in IP networks 47Table 1. Traffic Sources ParametersParameter VoIP LAN TransactionsPear rate, packets per second 10 164 0Mean call/session duration, sec 180 20 10Mean duration between calls/sessions, sec 360 10 15Mean talk/silence duration, sec 20 5 2Distribution of call/series duration Exp. Exp. Exp.Maximal waiting time, sec 0.00072 0.6 1Maximal number of waiting packets 210 1804 2Traffic sources 5000 500 1500Priorities High Medium LowPacket length, bytes 800 800 8003. Integrated Services. Integrated Services (IntServ) is a complextechnique often called protocol that ensures Quality of Service in IP networks.It is applied usually in access routers or switches and tries to serve packets fromdifferent services in different ways depending on the quality requirements. IntServclassifies services into three main classes depending on the traffic requirements[8]:• Elastic applications• Tolerant real-time applications• Intolerant real-time applicationsElastic applications are served with “best effort” discipline [16]. There isno guarantee of quality level . Tolerant real-time applications are delay sensitiveand usually require high bandwidth. A token bucket model with peak rate con-trol is a proper model for LAN emulation. Some authors propose token bucketwith series length and mean rate control as a model for more accuracy. Manyauthors propose cascaded queues as shown below [16]. Intolerant real-time ap-plications require low delays and almost guaranteed bandwidth. The model withtwo cascaded token buckets is used in this case [7]. The VoIP service is intolerantto quality degradation. The IntServ simulation model is based on two cascadedtoken buckets that bound the peak rate, series length and mean rate of the traffic(Fig. 1) [5, 6].The model from Fig. 1 is quite complicated for simulation. Due to thisreason it is approximated as a black box that changes the characteristics of the48 R. Goleva, M. Goleva, D. Atamian, T. Nikolov, K. GolevFig. 1. An approximation of a Black box IntServ modeldata into output data in a way specific for IntServ. As a result after approxima-tion and some calculations the simpler model is derived with one FIFO queue,priorities, fixed rate at the output and different limits for waiting times in thequeue. The approximation technique is called further on a quick approximationtechnique with static and dynamic changes of the system bounds and is repre-sented on Fig. 2. The accuracy of the derived model is not changed end-to-enddue to the fact that the interfaces at both ends are the same and with the samecharacteristics. The only thing changed during approximation is the internalpacket management due to the work of the IntServ. This is the model that hasbeen simulated further. Table 2 includes main data for model behaviour.Fig. 2. Final IntServ model with input data, bounds in waiting times and queue length4. Differentiated Services. Differentiated Services (DiffServ) is an-other quality management technique that is more applicable nowadays. Due to itsnature DiffServ applies its rules on aggregated traffic. After appropriate markingQuality of service system approximation in IP networks 49Table 2. Model characteristics in IntServParameter ValueQueue length, packets 2016VoIP queue length fraction, packets 210LAN queue length fraction, packets 1804Transaction queue length fraction, packets 2Maximal waiting time for VoIP, sec 0,000716Maximal waiting time for LAN, sec 0,6Maximal waiting time for transactions, sec 1Priority for VoIP HighestPriority for LAN MediumPriority for transactions Lowof the aggregated packets they are gathered in the way that is defined for theirclass. There are three types of services we try to highlight in this paper [14]:• Premium service—low delays, low losses, guaranteed bandwidth like VoIP;• Assured service—less requirements to the delays and losses in comparisonto the premium service like LAN emulation;• Olympic service—no time requirements at all like transaction exchange.The two cascaded queues from Fig. 1 are approximated again with onesingle queue as it was done for IntServ above. The architecture is shown in Fig. 2and calculated for DiffServ procedure. System parameters derived for DiffServare shown on Table 3.Table 3. Model characteristics in DiffServParameter ValueQueue length, packets 1840VoIP queue length fraction, packets 200LAN queue length fraction, packets 1640Transaction queue length fraction, packets 2Maximal waiting time for VoIP, sec 0,0303Maximal waiting time for LAN, sec 0,27876Maximal waiting time for transactions, sec 1Priority for VoIP HighestPriority for LAN MediumPriority for transactions Low50 R. Goleva, M. Goleva, D. Atamian, T. Nikolov, K. Golev5. RSVP. Resource Reservation Protocol (RSVP) is a technique usefulfor delay sensitive traffic like VoIP. Three types of services are identified for RSVP,as follows:• Wildcard filter—applied to gather maximal requirements for given interfacelike LAN emulation;• Shared explicit—applied to gather maximal requirements for the interfacetaking into account the called address. Transaction exchange is modeled asshared explicit service;• Fixed filter—full reservation for quality sensitive services like VoIPThe queue architecture for RSVP is shown on Fig. 2. After application of thequick approximation schema calculation of RSVP parameters is done and theresults are shown in Table 4. Similar results can be derived having any other VoIPsignaling protocol in the network like SIP or H.323. The signalling capabilities ofNSIS protocol will not change drastically the model parameters for VoIP servicein comparison to the one in Table IV. Connection establishment and connectionrelease procedures in almost all VoIP applicable protocols are similar.Table 4. Model characteristics in RSVPParameter ValueQueue length, packets 1840VoIP queue length fraction, packets 200LAN queue length fraction, packets 1640Transaction queue length fraction, packets 2Maximal waiting time for VoIP, sec 0,07508Maximal waiting time for LAN, sec 0,69Maximal waiting time for transactions, sec 1Priority for VoIP HighestPriority for LAN MediumPriority for transactions Low6. Results. The simulation is performed in the language C++. Thepseudo-exponential pseudo deterministic characteristics of the traffic sources arereached after combination of many random generators [9, 10, 11]. The behaviorof the queue is complex due to the priorities and limits on waiting times. Manyparameters have been derived from the model like probability of packet loss dueQuality of service system approximation in IP networks 51Fig. 3. Delay variation of packets in queue input and output for IntServ, DiffServ andRSVP52 R. Goleva, M. Goleva, D. Atamian, T. Nikolov, K. GolevFig. 4. Queue length in VoIPFig. 5. Queue length in LANFig. 6. Queue length in transaction exchangeQuality of service system approximation in IP networks 53to the lack of room in the queue, probability packets being dropped due to anexceeded waiting limit, probability to wait for different types of traffic, observa-tions on of the packet intervals, queue lengths, delay, delay jitter, waiting timesat many interface points in the model like output of the traffic sources, inputand output of the queue. The statistical accuracy of the derived results is provenby Student criterion. The presented results are in the 90% confidence intervalfrom a statistical point of view. IntServ, DiffServ and RSVP have different waysof dealing with packets and this influences the way they police, drop and shapethem. In Fig. 3 packet intervals at the input and output of the queue are shown.It is interesting for jitter and shaping effect estimation. The effect of faster ser-vicing in RSVP can be seen from a). The delay variation of the packet intervalsis becoming smoother and tends to a constant value. A similar result is visiblefor IntServ on b). On c) the shaping of the IntServ and DiffServ is seen again.Interesting results that influence directly interfaces and queue manage-ment are derived on the basis of queue length per service type. The queue frac-tion of the three services is observed. It is visible from Fig. 4 that for serviceswith highest priority like VoIP IntServ is the most proper mechanism. Fig. 5 andFig. 6 represent the same probability density function for LAN and transactionservices. Because of the less critical waiting times and low priority the natureof waiting times for LAN packets tends to be deterministic. In Fig. 6 graphs forIntServ and RSVP are overlapped.Table 5 represents mean values of the queue lengths per discipline andper service. They can be used for approximate planning of the time and spacelimits in the router interfaces.Table 5. Mean queue lengthMechanismMean queuelength, packetsMean queuelength of VoIPfraction, packetsMean queuelength of LANfraction, packetsMean queuelength of Transfraction, packetsIntServ 37.97523 1 35 2DiffServ 1586.961 2.24207 1583.561 1.98331RSVP 1798.409 156.9017 1639.675 27. Conclusion. In this paper we show observations on delay and delayjitter of packets in an IP router and queue length per service type. These resultsdemonstrate the specific characteristics of the device as a packet policing/shaping54 R. Goleva, M. Goleva, D. Atamian, T. Nikolov, K. Golevfacility in three QoS management algorithms: IntServ, DiffServ, RSVP. Thedeterministic nature of the packet streams suppress shaping and increases losses.The statistical multiplexing effect is limited due to the deterministic streams.The results demonstrate the capability of IntServ to ensure excellent ser-vice for its higher priority applications. It is promising in access networks. Diff-Serv shows excellent resource management and utilization and therefore is betterfor core services. RSVP is a good counterpart of IntServ in access networks. Itsdevelopment to NSIS protocol is going to be a powerful QoS assurance technique.The authors prove the capability of the quick approximation technique in fast in-terface bounds calculations and adjustments. The result is important in networkswith dynamic changes in quality of service requirements. The fractional shapingphenomenon demonstrated in the paper is important in small enterprise LocalArea Networks (LAN) or limited distance MAN. The authors refine the simula-tion model with more traffic sources and more precise generation of the packetsfrom these sources based on the observation of the real traffic. MMPP and geo-metric/Weibull distributions are also considered. Limits criteria and thresholddynamic calculation for queue management are also under investigation.REFERE NCES[1] Ahmed T., A. Mehaoua, R. Boutaba, Y. Iraqi. Adaptive PacketVideo Streaming Over IP Networks: A Cross-Layer Approach. IEEE Jour-nal on Selected Areas in Communications, 23, No. 2 (2005), 385–401.[2] Cheng G., L. Zhu, N. Ansari. A New Deterministic Traffic Model forCore-Stateless Scheduling. IEEE Transactions on Communications, 54, No.4 (2006), 113–119.[3] Deng D., H. Yen. Quality-of-Service Provisioning System for MultimediaTransmission in IEEE 802.11 Wireless LANs. IEEE Journal on SelectedAreas in Communications, 23, No. 6 (2005), 1240–1252.[4] Huang L., C. Jay Kuo. Joint Connection-Level and Packet-LevelQuality-of-Service Support for VBR Traffic in Wireless Multimedia Net-works. IEEE Journal on Selected Areas in Communications, 23, No. 6(2005), 1167–1177.Quality of service system approximation in IP networks 55[5] Goleva R. I., M. A. Goleva, D. K. Atamian, K. A. Golev. LANTraffic Shaping Analyses in Metropolitan Area Networks. ICEST 2007, 24–27 June, Ohrid, Macedonia, 303–306.[6] Goleva R., M. Goleva, D. Atamian, T. Nikolov, K. Golev. VoIPTraffic Shaping Analyses In Metropolitan Area Networks, Fifth Interna-tional Conference INFORMATION RESEARCH AND APPLICATIONSi.tech, 26–30 June 2007, Varna, 460–467. To appear in Journal on Infor-mation Technologies and Knowledge (IJ ITK) 2 (2008).[7] Jha S., M. Hassan. Engineering Internet QoS. Artech Hose, 2002.[8] Janevski T. Traffic Analysis and Design of Wireless IP Networks, ArtechHouse, 2003.[9] Kleinrock L. Queueing Systems, Volumes I and II. John Wiley and Sons,1976.[10] Iversen V. Teletraffic Engineering Handbook. ITU-D, 2005.[11] Lavenberg S. S. (Editor) Computer Performance Modeling Handbook.Academic Press, 1983, ISBN 0-12-438720-9.[12] Mirtchev S. Study of Queueing Systems with State Dependent MeanService Time. New Trends in Mathematics and Informatics, Jubilee Inter-national Conference, Sofia, July, 2007.[13] Mirtchev S. Study of Queueing Behavior in IP Buffers. iTech’07 Confer-ence, Varna, St. Kostantin and Elena, June, 2007.[14] Pitts J., J. Schormans. Introduction to IP and ATM Design and Per-formnce. John Wiley&Sons, Ltd., 2000.[15] Pachamanov, R., D. Pachamanova, B. Tsankov. An optimization-Based Connection Admission Control Method for IEEE 802.16 WirelessNetwork. In: Proc. of IEEE Africon 2007 Conference, Windhoek, Namibia,2007, 1–7.[16] Ralsanen V. Implementing Service Quality in IP Networks. John Wiley& Sons, Ltd., 2003.56 R. Goleva, M. Goleva, D. Atamian, T. Nikolov, K. Golev[17] Tsankov B., R. Pachamanov, D. Pachamanova. Modified BradyVoice Traffic Model for WLAN and WMAN. Electronics Letters, 43, is-sue 23 (2007), 1295–1297.Rossitza GolevaDepartment of TelecommunicationsTechnical University of Sofia8, Kl. Ohridski blvd1756 Sofia, Bulgariae-mail: rig@tu-sofia.bgMariya GolevaDepartment of Communication NetworksUniversity of BremenBremen, Germanye-mail: mgoleva@gmail.comDimitar AtamianDepartment of TelecommunicationsTechnical University of Sofia8, Kl. Ohridski blvd1756 Sofia, Bulgariae-mail: dka@tu-sofia.bgTashko NikolovDepartment of TelecommunicationsTechnical University of Sofia8, Kl. Ohridski blvd1756 Sofia, Bulgariae-mail: tan@tu-sofia.bgKostadin GolevBianor Ltd., Bulgariae-mail: kotseto@gmail.comReceived December 11, 2007Final Accepted February 26, 2008

Quality of Service System Approximation in IP Networks

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