Quality of service for voice over IP in networks with congestion avoidance

Transcription

1 Ann. Telecommun. (2009) 64: DOI 0.007/s Qualty of servce for voce over IP n networks wth congeston avodance Vtalo A. Reguera Evelo M. G. Fernandez Felx A. Palza Walter Godoy Jr. Eduardo P. Rbero Receved: 4 May 2008 / Accepted: 22 September 2008 / Publshed onlne: 22 October 2008 Insttut TELECOM and Sprnger-Verlag France 2008 Abstract Ths paper assesses the mpact of actve queue management schemes on the qualty of servce of voce over Internet protocol applcatons. A new analytcal method based on a fxed pont approach to estmate the end-user satsfacton s proposed. The results obtaned were valdated usng dscrete event smulaton technques. In all the studed cases, t was observed a great deal of agreement between the analytcal results and the results obtaned through smulaton. The theoretcal predctons, as well as the presented emprcal evdences confrm, as demonstrated n prevous works, that the use of actve queue management offers better qualty of servce than the tradtonal queue control mechansms used n Internet. From these results, we V. A. Reguera F. A. Palza Telecommuncatons Department, Central Unversty of Las Vllas, Santa Clara, Cuba V. A. Reguera e-mal: vtalo@uclv.edu.cu F. A. Palza e-mal: fapalza@uclv.edu.cu E. M. G. Fernandez (B) E. P. Rbero Electrcal Engneerng Department, Federal Unversty of Parana, Curtba, Brazl e-mal: evelo@eletrca.ufpr.br E. P. Rbero e-mal: edu@eletrca.ufpr.br W. Godoy Jr. Electrcal Engneerng Department, Federal Unversty of Technology-Parana, Curtba, Brazl e-mal: godoy@utfpr.edu.br may reasonably conclude that the presented method can be used for network desgn n the presence of voce traffc. Keywords VoIP Congeston avodance AQM Qualty of servce Internet Introducton Actve queue management (AQM) has been proposed as a tool for congeston avodance n Internet []. Several studes have been dedcated n the past few years to desgn and evaluate AQM algorthms [2 5]. AQM mechansms nteract at network routers sendng back nformaton to the traffc sources about the mmnence of congeston. Transport layer protocols lke the transmsson control protocol (TCP) have the capacty to react to these sgnals reducng data transmsson rate to avod network congeston. In many cases, the use of AQM helps to reduce network mparments such as delay, delay varaton (jtter), and packet loss that are produced durng the congeston perods. These lead to an ncrease n the qualty of servce (QoS) of those applcatons that are affected for the degradaton of these parameters. The complex nteracton between congeston control mechansms and the applcatons makes t analytcally dffcult to evaluate the mpact of AQM on the percepton that the end users have on the offered QoS. A prevous work [5] verfed through computatonal smulaton that adaptve verson of actve queue management algorthms ncreases n a sgnfcant way the customer-perceved qualty of phone calls transmtted through congested connectons.

2 226 Ann. Telecommun. (2009) 64: It s clear that n those scenaros where t s possble to establsh a dfferentaton of flows and to separate voce applcatons from other applcatons, a better choce s to use queues wth prorty (e.g., prorty queue, low latency queue) [6]. Ths guarantees for expedted sendng of voce packets wth a superor QoS than t would have wth any other mechansm. However, n heterogeneous networks, t s not always possble to establsh end-to-end QoS warrantes for voce applcatons. The results of ths research are applcable to those network segments where voce traffc s not dfferentated from remanng traffc. These scenaros are very common n Internet wth the prolferaton of voce over Internet protocol (IP) servces (VoIP) based on peer to peer archtectures as t s the case of Skype whose software has been wdely dffused [7, 8]. In ths work, an analytcal procedure s presented based on a fxed pont approach to determne the mpact of adaptve random early detecton (ARED) and adaptve vrtual queue (AVQ) n the qualty of servce of VoIP applcatons. The random early detecton (RED) algorthm s the most spread and mplemented AQM proposal n the Internet [2]. Ths algorthm sgnals the traffc sources about congeston mmnence by markng or dscardng packets wth a certan probablty dependng on the average queue sze. However, the overall performance of the orgnal verson of RED algorthm has shown to be very dependent on the confguraton parameters. The desred results may not be obtaned n some network scenaros [9, 0]. Its adaptve verson solves ths problem adjustng the control parameters of the algorthm n a dynamc way. The only requrement s to set the desred average queue sze or equvalently the average packet delay n the queue. The AVQ mechansm [] s based on the dea of mantanng a vrtual queue wth the same sze of the real queue, but wth less outflow capacty. For each nput packet, a fcttous packet s put n the vrtual queue whenever t s not full; otherwse, the packet s marked or dscarded. The model proposed n ths paper allows capturng the nteracton between AQM algorthms and sources of traffc, estmatng the values of the delay, jtter, and packet loss rate. Once obtaned, these network metrcs are used as nputs to the computatonal algorthm proposed n [2] that allows to estmate the amount of satsfacton experenced by users of a VoIP system. A detaled descrpton of the proposed analytcal method appears n the next secton. In Secton 3, the results obtaned by the analytcal method are contrasted wth the results obtaned through smulaton. Fnally, Secton 4 comments n the form of conclusons some crtera about the valdty and usefulness of the obtaned results. 2 Analytcal models The startng pont for the development of the mathematcal model s a congested lnk where some AQM mechansm s mplemented. Consderng an Internet router n whch TCP and user datagram protocol (UDP) flows are mxed, the nteracton between these Fg. Network dagram TCP Sources UDP Sources 2 n t 2 n u (t) (u) + Round-Trp Tme (RTT) p q T Queue Model C p q q Interconnecton Recevers E- Model (ITU-T G.07) MOS p: packet loss probablty q: average queue delay q: delay standard devaton (t) : TCP sources arrval rate (u) : UDP sources arrval rate T: overall arrval rate

3 Ann. Telecommun. (2009) 64: flows and the router AQM mechansm can be represented as llustrated n Fg.. TCP sources regulate the generated traffc dependng on the packet loss rate and on the round trp tme (RTT) experenced by the packets. On the other hand, UDP sources are unresponsve to the degree of congeston that the network presents. From the pont of vew of queung theory, the router can be consdered as a queue whose servce dscplne and servce tme wll be determned by the actng AQM mechansm and by the capacty of the output lnk. Dfferent from the analyss carred out n [3], n ths work t s taken nto account the feedback that exsts between the queue and the traffc sources. The steadystate soluton of the system (fxed pont) resulted from ts nherent nteracton s consdered. Ths dea was frst descrbed n [4] and later t has frequently been used to model the behavor of dfferent TCP mplementatons [5, 6]. Startng from an accurate model of the sources and the queue, the fxed pont corresponds to the operatng pont of the real network. The obtaned values of loss rate, average queue delay, and jtter serve as nputs to the E-Model [2] to estmate customers mean opnon score (MOS) for voce applcatons. Whle the analytcal models themselves are not new, ts combnaton and applcaton to VoIP traffc n networks wth congeston avodance had never been addressed n prevous works to the best of our knowledge. 2. Fxed pont approach Let λ (t) and λ (u) be the arrval rate generated by the -th TCP and UDP source, respectvely (see Fg. ). The total arrval rate, λ T,s n t λ T = n u λ (t) + = = λ (u) () where n t s the number of TCP sources and n u s the number of UDP sources. Let q (λ T ) and p (λ T ) the average queue delay and t (q, p ) the packet loss rate determned by the queue model and the throughput of the -th TCP source. Assumng that the arrval rate of the UDP sources s constant and s not affected by network congeston, then the operatng pont can be found n such a way that satsfes n t = n u t (q (λ T ), p (λ T )) + = λ (u) = λ T (2) Equaton 2 s of the form f (x) = x where λ T s a fxed pont. Dependng on the queue model used, t s not always possble to fnd a compact expresson for q (λ T ) and p (λ T ). The soluton of Eq. 2 can be found usng computatonal methods (e.g., usng Brent s method). 2.2 Source model Snce UDP protocol does not have the ablty to adapt the transmsson rate to the predomnant network condtons, the arrval rate from sources assocated to ths protocol wll be consdered constant. Ths can be the case of a voce codec generatng equal sze packets at a constant rate (e.g., G.7). On the other hand, sources based on TCP protocol wll adapt the transmsson rate dependng on the amount of congeston actually experenced. In what follows, a wdely used model for steady-state throughput estmaton of TCP sources proposed n [7] wll be consdered. Ths model s applcable to TCP Reno sources, one of the most wdespread n Internet [8]. Here the throughput s a functon of the loss rate and the round trp tme experenced by the lnk. Usng the same prevous notaton and n accordance wth [7], the throughput of a TCP source can be approxmated by the followng expresson t (q, p ) = mn W m, RTT 0 + q 2bp (RTT 0 + q ) + T 3 o mn (, 3 3bp 8 ) ( ) (3) p + 32p 2 where RTT 0 s the round trp tme wthout takng nto account the delay produced by the queue, W m s the maxmum wndow sze confgured at the recever (measured n segments), T o s the watng tme before the frst retransmsson and b s the number of packets that are acknowledged by ACK (typcally two). 2.3 Drop-tal queue model In order to consder the benefts that can be obtaned by usng AQM n the presence of voce traffc, frstly t s necessary to establsh the network behavor when a tradtonal queue mechansm such as drop-tal (DT) s

4 228 Ann. Telecommun. (2009) 64: used. A DT queue can be analyzed wth classc models of queung theory. In ths case, the model can be descrbed usng the abbrevated Kendall notaton [9]asa G/G/K + queue. That s, a queue wth arrval process and servce tme characterzed by a general dstrbuton, only one server and capacty for K packets n the queue. Consderng a lnk wth constant capacty, the servce tme dstrbuton wll be determned by the dstrbuton of the packets sze. Studes accomplshed n the Internet [20, 2] show that ths dstrbuton s predomnantly trmodal due to the combnaton of the TCP acknowledgments (ACK) and the prevalence of two maxmum transmsson unts (MTU). However, ths dstrbuton can vary as a consequence of the contnuous appearance of new applcatons [2]. In the smplfed model of Fg., ths dstrbuton wll be characterzed by the combnaton of the TCP maxmum segments sze (MSS) and by the sze of the frames generated by the voce codecs and other sources of unresponsve traffc. The arrval process dstrbuton s much more dffcult to be descrbed due to the bursty nature of the data packets that characterzes many of the traffc sources n the Internet. Some studes [22] evdence that Internet traffc exhbts a self-smlar behavor, n other words, the statstcal traffc patterns have smlar characterstcs when they are analyzed n dfferent tme scales. These observatons have led many researchers to avod the use of classc models of queung theory. However, recently, these models have been revsted wth acceptable results n the analyss of some Internet systems [4, 5, 23]. Specfcally, n [2], t s demonstrated that the traffc produced by the overlap of a consderable number of sources tends to behave as a Posson arrval process. Consderng a hgh multplexng level n the nput of the congested lnk, the aggregated traffc can be approxmated by an exponental dstrbuton. As a result, ths system becomes an M/G/K + model. For an M/G/K + queue, the probablty of exstng j packets n the system n an arbtrary steady-state tme nstant, P DT j, can be computed (usng classc results of queung theory, [9]) as P DT j = H j, j = 0,,...K (4) + ah where a s the offered traffc and H j and H are obtaned as follows: ( H j+ = H j ψ j ) j ψ j + H ψ 0, = j = 0,,...K (5) H = + j H j (6) = beng H 0 = and ψ j the probablty that j packets arrve durng the servce tme. The loss probablty s obtaned as: p = P DT K+ = K j=0 P DT j = H + ah (7) Usng Eq. 4 and Lttle s formula [9], the average queue watng tme s: K+ = ( j ) PDT j q = (8) λ T ( p) In the above model, the loss probablty and the average queue watng tme are the same for all traffc sources so, n Eqs. 7 and 8 the subscrpt has been omtted (p = p and q = q). In practce, there can be dfferences between the values of loss and delay experenced by packets sent n burst from some sources. The valdty of ths and other approaches ncluded n the model wll be analyzed and contrasted wth expermental results n the next secton. Jtter s estmated from the standard devaton of the packet delay. Let σq 2 denote the system queue watng tme varance. Consderng that a packet that enters n the queue should wat untl all prevous packages n the system were served, and usng the results from [24] leads to σ 2 q = Qσ 2 h + σ 2 Q h2 (9) where Q s the average number of packets n the system, σq 2 the varance of the number of packets n the system, h the average servce tme, and σh 2 the varance of the average servce tme. From Eq. 9, the jtter experenced by the -th traffc source can be computed as σ = σq 2 + σ h (0) where σ h s the average servce tme varance of the -th traffc source. The network equlbrum pont can be found usng Eqs. 2, 3, 7, and8. Once the fxed pont s determned, the values obtaned through Eqs. 7, 8, and0 are used to estmate the degree of end-user satsfacton of VoIP systems. 2.4 ARED queue model When ARED s mplemented n the router queue, the packet loss rate wll be condtoned by the dscard

5 Ann. Telecommun. (2009) 64: probablty functon of the algorthm, n accordance wth ( ) 0, l q < mn th p RED lq =, ( l q > max th () lq mn max th p, elsewhere max th mn th ) where mn th and max th are the maxmum and mnmum average queue sze threshold between whch the dscard probablty has a lnear behavor wth postve slope, max p s the maxmum probablty value n that nterval and l q s the average queue sze, updated from the nstantaneous queue sze, I q, n the followng way l q ( w q ) lq + w q I q, 0 <w q < (2) where w q s a weghtng factor that ndcates the weght that the most recent sample (I q ) has n relaton to the cumulated average l q. The steady-state probablty of queue occupaton can be obtaned usng a Markov chan and approxmatng the arrval process and servce tme dstrbutons by exponental dstrbutons. In ths case, the process becomes a brth death process. By approxmatng the average queue sze by the nstantaneous queue sze, the steady-state probablty of havng j packets n the system can be computed as a j j ( p RED (l)) P ARED l=0 j = + K+ a ( p RED (l)) = l=0 (3) where a s the offered traffc. One needs to take nto account the adaptve behavor of ARED n the computaton of Eq.. The parameter max p s adjusted accordng to the arrval rate followng the gudelne presented n [25]. The ARED algorthm can be confgured to dscard all the packets wth equal probablty (packet mode) or to dscard packets dependng on ts sze n bytes (byte mode). In the frst case, the packet dscard rate wll be the same for all the traffc sources and can be obtaned as p = K+ j=0 P ARED j p RED ( j ) (4) When ARED s confgured n byte mode the loss rate wll depend on the sze of the sent packets so the loss rate for the -th source s computed as p = λ Th a K+ j=0 P ARED j p RED ( j ) (5) where h s the average servce tme for the -th source. The queung delay experenced by packets from the -th traffc source can be computed from Lttle s formula as q = K+ j=0 ( j ) P ARED j λ T ( p) 2.5 AVQ queue model (6) For the case of a queue wth AVQ mechansm, the model was obtaned n a dfferent way due to the specfc characterstcs of ths AQM algorthm. In Fg. 2, a block dagram of an AVQ queue model s shown. Here the system s composed of two queues, the frst one representng the vrtual queue whch determnes the loss probablty. Traffc that s not dscarded wll wat on the second queue untl beng served. Gven that the lnk capacty of the frst queue s less than the lnk capacty of the whole system, the second queue practcally does Fg. 2 AVQ queue dagram AVQ Queue Model M [X] /M/K+ Queue M/G/ T ~ C = T ( -p) C p q q

6 230 Ann. Telecommun. (2009) 64: not experence congeston; therefore, the delay s small n comparson wth a DT queue. When the AVQ algorthm s confgured to measure the sze of the queue n packets, t can be used (Eq. 7)to determne the loss probablty wth the dfference that, n ths case, the lnk capacty s, C = ρc where ρ s the desred utlzaton. However, the predetermned confguraton of the algorthm measures the sze of the queue n bytes (note that a steady-state analyss s beng made and the adaptaton mechansm developed n AVQ ensures that the lnk utlzaton fulfls the desred utlzaton). Ths mples that bgger packets wll experence hgh loss rates when the queue begns to saturate. In order to take nto account ths effect, the queue s represented usng a batch arrval model of the form M [X] /M//K +, wherex s the random varable for the number of octets n the packet and K s the maxmum queue sze, both expressed n bytes. In ths case, the probablty dstrbuton of the steady-state system, P ()AVQ, can be found through the followng expressons: K+ P ()AVQ = P ()AVQ 0 a K+ P ()AVQ 0 + j= P ()AVQ j P ()AVQ K+ = = ( = P ()AVQ j + j 2 K = =0 ) K+ a = P ()AVQ a j P ()AVQ a K+ P ()AVQ j = (7) where, a n = λ n /μ, λ n s the arrval rate of the n-sze packets and μ s the recprocal of the tme requred for the transmsson of byte. Assumng that all packets produced by the -th traffc source have the same sze n, the loss probablty wll be p = K+ j=k+2 n P ()AVQ j (8) The traffc that s not dscarded enters on the second queue wth an arrval rate λ = λ T ( p). The packet average watng tme can be calculated substtutng n Eq. 8 the steady-state system probablty dstrbuton by that of an M/G/ model whch s a partcular case of DT for K =. In the same way, the jtter can be estmated usng Eq Multple congested lnks and other consderatons In an Internet voce communcaton, packets travel through multple lnks. Dependng on the route, congeston can occur n more than one lnk. The prevously descrbed model for a congested lnk can be easly extended to a topology wth multple lnks. In ths case, the network can be represented by a set of cascaded queues where the traffc experenced by a partcular lnk wll enter the next queue beng mxed wth the traffc generated by another set of sources. Applyng for each queue the procedures descrbed n the prevous subsectons, the operatng pont of the whole system n steady state can be found. The loss probablty, the average watng tme, and the end-to-end jtter consderng m queues can be computed, respectvely, as p = m ( p m ) (9) q = q m (20) m σ = σm 2 (2) m The proposed model assumes a relable data lnk layer (.e., packet loss and sgnfcant delay varatons are located at the queue of the networks nodes due to traffc congeston) as well as stable routng mechansms. The above s true for most wred networks but t could be dfferent for certan types of wreless networks. Partcularly, wreless mult-hop ad hoc networks (e.g., IEEE 802.) can suffer sgnfcant packet drops at data lnk layer and re-routng nstablty. In such cases, the model should be modfed to reflect these behavors. 2.7 MOS calculaton Mean opnon score (MOS) [26] has been tradtonally used to measure subjectve percepton of voce communcatons. MOS s gven on a scale of 5, where a hgher value corresponds to better qualty. Snce MOS s a subjectve test dffcult to be carred out n practcal stuatons, some other objectve tests have been developed. ITU recommendaton G.07 descrbes E-model, a computatonal algorthm that ncorporates mparment factors present n modern transmsson networks. The output of E-model s a scalar qualty ratng value, R, whch s computed as R = R o I s I d Ie eff + A (22) where R o represents, n prncple, the basc sgnal-tonose rato, I s s a combnaton of all mparments that

7 Ann. Telecommun. (2009) 64: occur more or less smultaneously to the voce sgnal, I d represents the mparments caused by the delay, Ie eff s the effectve equpment mparment factor and A s an advantage factor. From the calculaton of R based on network metrcs, the correspondent MOS value, MOS CQ, for conversatonal qualty can be derved as MOS CQ = R + R (R 60)(00 R) (23) Equaton 23 s vald for 0 < R < 00. ForR < 0, MOS CQ = and for R > 00, MOS CQ = 4.5. Table shows the relatonshp between the R-value of the E-model, the MOS score and the equvalent degree of user satsfacton. Wth the use of standard parameters [2], the results that characterze a hgh qualty voce transmsson are obtaned wth R = Takng nto account only network mparments such as delay, jtter, and packet loss, Eq. 22 canberewrttenas R = 93.2 I d Ie eff (24) Assumng that perfect echo cancellaton exsts n the network, the delay degradaton factor s reduced to zero when one-way delay s below or equal to 00 ms (T a 00 ms). For T a > 00 ms, I d s computed as { ( I d = 25 + x 6 ) [ ( 6 x ) ] } (25) 3 where x = log ( T a ) 00 (26) log 2 The parameter T a s composed by the sum of all the delays that occur from the ntaton of a voce sgnal untl the recepton and dentfcaton of the dentcal sgnal at the receved output speaker. It ncludes delay ntroduced by the codec and the tme necessary to accomplsh all operatons untl a packet s sent. Once a voce packet s put n the network, t wll suffer delays due to the propagaton tme of the physcal communcaton channels, the transmsson delay assocated Table Relatonshp between the R factor and MOS score User satsfacton R factor MOS (lower lmt) (lower lmt) Very satsfed Satsfed Some user dssatsfed Many user dssatsfed Nearly all user dssatsfed to channel capacty, and the delay ntroduced by network nodes. Packets whch arrve at ts destnaton stll have to experence delays produced by the unpackng and decodng processes and one more addtonal delay caused by the jtter buffer at the recever. In the model presented n ths paper, the value of T a s computed as T a = T 0 + q + f (σ ) (27) where T 0 s the end-to-end delay wthout ncludng the delays n the queue (whch depends bascally on the topology and the lnks transmsson parameters) and f (σ ) s a functon of the jtter that depends on the algorthm used n the jtter buffer at the recever. Throughout ths paper t wll be used f (σ ) = 4σ (28) In all the studed scenaros, the use of Eq. 28 guarantees a suffcently wde margn to absorb the jtter produced n the queues of the routers wth a neglgble dscard rate. The degradaton factor ntroduced by the use of low bt rate codecs, Ie, s tabulated n ITU-T recommendaton G.3 [27]. The value of the effectve equpment mparment factor when the encoders operaton s under condtons of random packet loss s computed as p Ie eff = Ie + (95 Ie) (29) p + B pl where the packet loss robustness factor, B pl, s defned as a codec-specfc value n [26] andp s computed through Eqs. 7, 4, or 8 dependng on the AQM algorthm under consderaton. 3 Results and dscusson In ths secton, the results obtaned by applyng the analytcal procedure proposed above to obtan the mean opnon score for voce applcatons are presented. Three network scenaros are consdered provdng elements about the valdty of the proposed theoretcal model and about the performance of some AQM schemes n the presence of voce traffc. 3. Experment setup The network scenaro was composed by a congested connecton where voce applcatons were mxed wth a varable number of TCP sources as shown n Fg. 3 The round trp tme for TCP connectons were vared n the range of 20 to 400 ms. The maxmum TCP s segment sze was set to,000 bytes whch s approxmately the average MSS value commonly observed

8 232 Ann. Telecommun. (2009) 64: r 0 Mbps varable delay 2 Mbps 5 ms 0 Mbps, 2 ms r sources. In dong so, UDP sessons were establshed wth a payload of 92 bytes per packet, 80 of them correspondng to the audo samples and 2 to the realtme transport protocol header. Packets were sent at a rate of 00 packets/s to smulate a G.7 codec. Smulatons were conducted usng the network smulator. Each smulaton was replcated startng calls sequentally by ntercalatng dle perods of 0 s between the end and the begnnng of consecutve calls. The number of replcatons was adjusted to obtan estmatons wth a confdence nterval of less than 5% of the mean value wth 95% confdence. long-lved traffc nodes short-lved traffc nodes Fg. 3 Network smulaton topology VoIP termnal unresponsve traffc node n the Internet [23, 26]. The AQM mechansm under study was actvated n routers havng a queue capacty of 20 packets. The target queue sze was set to 20 packets. Ths represents an average queue delay of approxmately 40 ms consderng a mean packet sze of 500 bytes and a lnk capacty, C, of 2 Mbps. The voce calls were smulated usng constant bt rate traffc 3.2 Predomnant TCP background The MOS values obtaned for dfferent AQM mechansms are shown n Fg. 4. The number of TCP sources was ncreased to observe the performance of the algorthms n condtons of severe congeston. The results obtaned usng the fxed pont approach are represented n contnuous lnes whle the dashed lnes show the smulaton results. In all cases, a great smlarty s observed between the analytcal results and the results of the smulaton. For the case of a DT queue, the qualty of the voce calls degrades wth the ncrease of the number of TCP sources; Fg. 4 Analytcal and smulated MOS values MOS 2 0 DT ARED AVQ Number of TCP flows

9 Ann. Telecommun. (2009) 64: Fg. 5 Cumulatve probablty functon of the queue smulated theoretcal Cumulatve Dstrbuton Functon AVQ ARED DT Queue length t s predctable that wth the ncreasng congeston the packet loss rate and the delay wll ncrease too and, therefore, the MOS values wll decrease. However, when ARED s mplemented and confgured n byte mode, the loss rate experenced by the voce packets s low when compared wth that of the TCP packets that have bgger sze (MSS =,000). Furthermore, as t s shown n Fg. 5 the mean value of the queue occupancy dstrbuton s near the desred value of 20 packets. As a result, the MOS acheved wth ARED corresponds to a hgh user satsfacton and, as t s verfed n Fg. 4, ths result holds when the number of responsve traffc sources s ncreased. When the AVQ algorthm s mplemented n the routers, the results are even better because the loss rate and the queue average watng tme are smaller n comparson wth ARED. The AVQ confguraton used measures the sze of the queue n bytes so, bgger packets wll be more affected for the loss probablty whle the queue occupaton s mantaned n low levels. As n ARED, wth the use of AVQ the qualty of voce calls does not suffer wth the ncrease of the number of TCP sources. These results confrm the statements done n [3] about the benefts that real tme applcatons receve from AQM mechansms. The above-mentoned s vald for the analyzed network scenaro, whch reflects some of the typcal characterstcs of the global network lnks. However, due to the heterogenety of the Internet, these characterstcs can vary n some network segments. In the next subsecton, the performance of the AQM algorthms s analyzed when the traffc pattern s altered. 3.3 Varatons n TCP MSS In the prevous subsecton, t was consdered a TCP maxmum segment sze of,000 bytes whch corresponds to the average MSS value observed n the Internet [2]. Snce apparently the benefts obtaned by usng AQM are much related to the bas of the packet loss rate due to bgger packets, t would be reasonable to hope that the end user-perceved voce Table 2 MOS for dfferent values of maxmum segment sze DT ARED AVQ MSS, , , Analytcal Smulaton

10 234 Ann. Telecommun. (2009) 64: Fg. 6 Occupancy probablty functon of the DT queue for dfferent values of MSS MSS = 000 Occupancy Probablty Functon MSS = 500 MSS = Queue Sze (bytes) Fg. 7 MOS value wth unresponsve traffc (analytcal and smulated) 5 4 MOS 3 2 DT ARED AVQ Unresponsve traffc (% of the lnk capacty)

11 Ann. Telecommun. (2009) 64: Fg. 8 MOS values when ARED s n packet mode and the AVQ queue s measured n packets MOS 2 0 DT ARED (packet mode) AVQ (queue n packets) Number of TCP flows qualty may degrade as the sze of the TCP packets becomes comparable to the sze of the voce packets. Table 2 shows the values of the mean opnon score for dfferent maxmum segment sze values obtaned wth 00 homogeneous TCP sources (.e., wth dentcal confguraton parameters). When the sze of the segments s decreased, the transmsson tme s also decreased. Ths leads to an ncrease n the servce rate. As a result, a decrease n the queue average watng tme s produced n all cases. In partcular, for a DT queue, ths decrease s sgnfcant and, together wth the dsplacement of the queue occupaton dstrbuton movng away from ts physcal lmt and causng less packets loss (see Fg. 6), t produces a gradual ncrement of the MOS as the value of the TCP maxmum segment sze decreases. The AQM schemes contnue offerng a satsfactory qualty of servce for the VoIP applcatons. Ths means that ARED as well as AVQ are able to satsfy ther desgn parameters wthout mposng a notceable packet loss rate (nferor to %) for the users of the voce servces. Clearly, f the number of sources s ncreased t s more dffcult to regulate ts transmsson rate and, consequently, a deteroraton of the qualty of servce wll take place n all the cases. 3.4 Increasng the unresponsve traffc In the prevous example, the traffc that was mxed wth the voce applcatons was composed exclusvely for TCP-based traffc flows. In ths sense, a good performance of the AQM algorthms s predctable snce they were projected bascally to work wth ths knd of traffc. The next experment ncludes the presence of unresponsve traffc n conjuncton wth the TCP-based traffc flows. Table 3 Relatve error for dfferent experments Experment DT ARED AVQ Mean Max Mean Max Mean Max Predomnant TCP background (Fg. 4) Varatons n TCP MSS (Table 2) Increasng the unresponsve traffc (Fg. 7) Changes n AQM operaton (Fg. 8)

12 236 Ann. Telecommun. (2009) 64: Fgure 7 shows the MOS values n the presence of a growng number of UDP flows that represents an amount of traffc up to half the avalable capacty. The number of TCP sources was fxed to 50. Once agan, an approprate correspondence between the analytcal results and the results of the smulaton s notced. ARED and AVQ provde a satsfactory qualty of servce whle DT exhbts a bad performance. In all cases, qualty degradaton s not observed wth the ncrease of the unresponsve traffc. Ths s due to an unequal dstrbuton of the avalable capacty, scenaro n whch the UDP sources are benefted. TCP connectons reduce ther transmsson rate allowng unresponsve traffc to occupy a larger bandwdth. 3.5 Changes n AQM operaton All the prevous results were obtaned operatng ARED n byte mode and expressng the sze of the AVQ queue n bytes. These parameters were then altered n the confguraton of the AQM algorthms to observe ther effect n the qualty of servce of the voce applcatons. Fgure 8 shows the QoS degradaton when ARED s confgured n packet mode. Now the chances for a flow to have a packet dscarded s related to the number of transmtted packets and not the number of transmtted bytes. The loss rate experenced by the voce traffc s comparable to that of the TCP packets and rses wth the ncrease of the traffc payload. As a result, ARED exhbts a smlar performance to that observed wth DT. In the case of AVQ, the modfcaton n the queue measurement unt dd not sgnfcantly alter the qualty of servce of VoIP. In all conducted experments, t was verfed that the theoretcal predctons match the smulaton results wth acceptable accuracy. Table 3 shows the values of the mean and maxmum relatve error. The worst case was observed usng DT whle ncreasng the number of unresponsve traffc wth a mean and maxmum relatve error of 4.44% and 6.34%, respectvely. The curves n Fg. 7 show that the analytcal model tends to be pessmstc and therefore n ths case the approxmaton errors could be consdered as a safety margn for desgn purposes. 4 Conclusons Smulaton tools have been tradtonally used to evaluate AQM performance n complex scenaros. In ths paper, a new analytcal method was proposed to evaluate the mpact of actve queue management on the qualty of servce of voce over IP systems. The numerc examples above descrbed evdence that the proposed analytcal model for estmatng the user satsfacton gves smlar results to that obtaned through computer smulaton. The results agree wth prevous works evdencng that the use of some AQM schemes provde a better voce qualty than tradtonal drop-tal queues. The proposed method can assst n the deployment of VoIP systems, provdng useful nformaton to network desgner wth a hgh degree of accuracy n a costeffectve manner. References. Network Workng Group (998) RFC 2309 recommendatons on queue management and congeston avodance n the nternet. Aprl 2. Floyd S, Jacobson V (993) Random early detecton gateways for congeston avodance. IEEE/ACM Trans Netw (4): Hollot C, Msra V, Towsley D, Gong W (2002) Analyss and desgn of controllers for AQM routers supportng TCP flows. IEEE Trans Automat Contr 47(6): Huggard M, Robn M, Btorka A, McGoldrck C (2004) Performance evaluaton of farness-orented actve queue management schemes. In: Proceedngs of IEEE nternatonal symposum on modelng, analyss, and smulaton of computer and telecommuncaton systems (MASCOTS). Volendam, The Netherlands, pp Reguera VA, Palza FA, Fernandez EMG, Godoy Jr W (2008) On the mpact of actve queue management on voce over IP qualty of servce. Comput Commun (): Szget T, Hattngh C (2004) End-to-end QoS network desgn: qualty of servce n LANs, WANs, and VPNs. CISCO, Indana 7. Bu T, Lu Y, Towsley D (2006) On the TCP-frendlness of VoIP traffc. In: Proceedngs of IEEE Infocom. Barcelona, Span, pp 2 8.TaoS,XuK,EstepaA,FeT,GaoL,GuernR,KuroseJ, Towsley D, Zhang Z (2005) Improvng VoIP qualty through path swtchng. In: Proceedngs of IEEE Infocom. Mam, FL 9. Floyd S, Kohler E (2003) Internet research needs better models. SIGCOMM Comput Commun Rev 33(): May M, Bolot J, Dot C, Lyles B (999) Reasons not to deploy RED. In: Proceedngs of the 7th nt. workshop on qualty of servce (IWQoS 99). London. Kunnyur S, Srkant R (2004) An adaptve vrtual queue (AVQ) algorthm for actve queue management. IEEE/ACM Trans Netw 2(2): The E-model (2005) A computatonal model for use n transmsson plannng. ITU-T Recommendaton G May M, Bonald T, Bolot J (2000) Analytc evaluaton of RED performance. In: Proceedngs of IEEE Infocom, vol 3. Tel Avv, Israel, pp Casett C, Meo M (2000) A new approach to model the statonary behavor of TCP connectons. In: Proceedngs of IEEE of Infocom, vol. Tel Avv, Israel, pp Casett C, Meo M (200) An analytcal framework for the performance evaluaton of TCP Reno connectons. Comput Netw 37(5):

13 Ann. Telecommun. (2009) 64: Bu T, Towsley D (200) Fxed pont approxmatons for TCP behavor n an AQM network. In: Proceedngs of ACM Sgmetrcs. Cambrdge, Massachusetts, pp Padhye J, Frou V, Towsley D, Krusoe J (998) Modelng TCP throughput: a smple model and ts emprcal valdaton. In: Proceedngs of ACM SIGCOMM 98. Vancouver, Brtsh Columba, pp Padhye J, Floyd S (200) On nferrng TCP behavour. In: Proceedngs of ACM SIGCOMM. Ago San Dego, Calforna, pp Gross D, Harrs CM (998) Fundamental of queung theory, 3rd edn. Wley, NY 20. Thompson K, Mller G, Wlder R (997) Wde area nternet traffc patterns and characterstcs. IEEE Netw 6: Fralegh C (2003) Packet-level traffc measurements from the sprnt IP backbone. IEEE Netw 7(6): Chakraborty D, Ashr A, Suganuma T, Keen GM, Roy TK, Shrator N (2004) Self-smlar and fractal nature of nternet traffc. Int J Netw Manag 4: Cao J, Ramanan K (2002) A Posson lmt for buffer overflow probabltes. In: Proceedngs of IEEE INFOCOM. New York 24. Tjms HC (994) Stochastc models: an algorthmc approach. Wley, New York, NY 25. Floyd S, Gummad R, Shenker S (200) Adaptve RED: an algorthm for ncreasng the robustness of RED s actve queue management. adaptvered.pdf 26. Methods for subjectve determnaton of transmsson qualty, ITU-T Recommendaton P.800 (996) 27. Provsonal plannng values for the equpment mparment factor Ie and packet-loss robustness factor Bp, ITU-T Recommendaton G.3, Appendx I (2002)