In this paper we use a previously developed RED (random early detection) model to analyze and develop a quantitative approach of defining one of the RED parameters, average queue weight, while the network load level varies. First, we introduce the linear control system and the pre-developed RED model. Based on this model, we next develop a proposition aiming at the RED parameter, average queue weight, and the load level only, to ensure the system stay stable. Our research is intended to provide a quantitative basis for parameterizing RED while load level varies. We present ns simulations to support our analysis

Shuang-Hua Yang (1257780)

Wu Chen (569414)

Yunqiu Li (789816)

English

Loughborough University Institutional Repository

   This item was submitted to Loughborough’s Institutional Repository (https://dspace.lboro.ac.uk/) by the author and is made available under the following Creative Commons Licence conditions.      For the full text of this licence, please go to: http://creativecommons.org/licenses/by-nc-nd/2.5/  Proceedings of the 2007 IEEE International Conference on TuesM02Networking, Sensing and Control, London, UK, 15-17 April 2007An Average Queue Weight Parameterization in aNetwork Supporting TCP Flows with REDWu CHEN, Yunqiu LI, Shuang-Hua YANGComputer Science Department, Loughborough Universitygood performance in real network scenarios in [7], [8], andAbstract- In this paper we use a previously developed RED [9]. In particular, one of the goals of RED is to stabilize the(Random Early Detection) model to analyze and develop a queue lengths in the routers, but the current version of REDquantitative approach of defining one of the RED parameters, does not succeed in this goal because the stead-state queueaverage queue weight, while the network load level varies. First, gwe introduce the linear control system and the pre-developed lenth ongly de o n the numberpofsactiveTCRED model. Based on this model, we next develop a proposition connections. On the other hand, the system response time isaiming at the RED parameter, average queue weight, and the also an aspect to measure the RED stability, as the quicker theload level only, to ensure the system stay stable. Our research is queue gets to a stable length, the better the system wouldintended to provide a quantitative basis for parameterizing RED perform.while load level varies. We present ns simulations to support our Parameter setting remains a critical open problem todayanalysis. [10] in other AQM or AQM-based approaches, such asIndex Terms- Parameterization, AQM, RED, Congestion Dynamic-RED (DRED), Stabilized-RED (SRED), BLUE andControl adaptive virtual queue (AVQ). Based on extensive simulationsand empirical investigations Floyd et al. [11] have provided aguideline of parameter settings as well as some recommendedI. INTRODUCTION parameters values for RED; However, the parameters are stillT 1 HE Internet is undergoing a major change in size, tightly coupled with each other and they need to be selecteddiversity, and reach, and impacting all aspects of our very carefully; otherwise, the performance of RED will belives. It is now the largest man-made interconnected system in degraded. Therefore, to analyze the parameterization of REDthe world. Congestion control plays a very important role in from a quantitative point of view would be a consequentialthe Internet to ensure good network performance. Internet research to take place.congestion control comprises two main parts: TCP The rest of the paper is organized as follows. In Section II,(Transmission control protocol) and AQM (Active Queue we analysis the pre-developed model and develop our stableManagement). Although many AQM algorithms have been RED proposition. In Section 111, we develop our algorithmproposd or taiming at the RED parameter, average queue weight, and theDetection) [3], BLUE [4], GREEN (Generalized Random load level only. We next present simulation results obtainedDeaevaion) Network)E [5], aREM (.Random by using Network Simulator 2 (NS2) that verify our analysisEal Evso Newr)[] n E Rno xoeta in Section IV. Finally, we present our conclusions in SectionMarking) [6], only RED AQM has been implemented in major iocommercial routers. However, parameterizing RED to obtain V.good performance under variable congestion scenarios is verydifficult. Such difficulties discourage network administratorsfrom turning on RED in their routers. Instead, they simply usethe default and primitive queue management function, DJroptail, which is not efficient and leads to higher than necessary A. Modelpacket loss. Certainly, there are many parameter tuning Accordin to a dyamic model of TCP congestion controltechniques for RED proposed in the literature, but they are g ryn gonly applicable under restrictive assumptions, e.g., constant [1],anfedb c o t R w nnumber of greedy TCP connections, etc. presented n [2].The performance ofRED is highly sensitive to its parameter wsettings.It has even been debated whether RED can deliver - ( (Wu CHEN, Yunqiu LI are research students with the Computer Science ______________________________Department, Loughborough University, LE1 1 3TU; Fig. 1. Feedback control system ofRED{W.Chen2,Y.Li3 }@glboro.ac.ukShuang-hua YANG is Professor with the Computer Science Department, whereLoughborough University, LEt1 1 3TU; S.H.Yang@lboro.ac.uk1-4244-1076-2/07/$25.00 ©2007 IEEE 590Authorized licensed use limited to: LOUGHBOROUGH UNIVERSITY. Downloaded on January 23, 2009 at 11:08 from IEEE Xplore.  Restrictions apply.&VW = W - WO; load N increases the high-frequency gain leading to decreased6= p - p . stability margins and increased oscillatory response.°q ,,; Conversely, large TCP loads will tend to damp the closed-loop= q - q0; transient response.W= expected TCP window size (packets); B. Stabilit Propositionp = probability of packet mark/drop; vq = expected queue length (packets); According to [2], we derive a simplified version of stability(W, q0, p0 ) the operating point defined by proposition in this section. We assume a scale for the numberof TCP sessions, say all N> N and all Ro < R+.WO0 andqO0.In Fig. 1, Cred (s) denotes RED control strategy, Stability Proposition: Let L and K satisfy:e sR denotes the delay term, PCp (s) denotes the linearized L (R+C)3 CoLred(RC) COTCP dynamics and Pqueu (s) denotes the queue dynamics. 2N < K (3)They were given in the following equations: where__ )gOli{(R+)'C'R+}(42N2 COg = 0.1Imin (4)2Ns+ 2C Then, the linear feedback control system in Fig. 1 is stable.RoCN Proof: Consider the frequency response ofthe compensatedPqueue (s) = Ro(1) loop transfer function, we have5+ |Ro L(jca) = Cred (jO)P(jO)C (s)= Lred L (RoC)3 e-jRoCred (S) /K+e red (2N)2J 1)( +1) +where K R2C +K R CLred Pmax (R C)3maxth -minth, LK= loge (1-a) r (e2N) VCVE 0[0 COg] (5)aO +1K jw 9~~~~~~~~~maxth = maximum threshold Kminth = minimum threshold Given any N . N- and R0 < R+a =Average queue weight L (R C)3 L (R C)36 =Sample time (2N ) (2N )R =Round-trip timeN= Load factor (number ofTCP sessions); ___ +1 CC= Link capacity (packet/sec); K2 KWe assume N(t) N and R(t) Ro as constants. From this and (3) it followsThen we have the following plant transfer function L(jag <1 (6)describing how packet-marking probability dynamically We again use (4) to obtainaffects the queue length. L (R+C)3(2N) ZL(j))>_ (2 o-(Ro >-900-0.1 >-180°P(s) = Pcp(s)Pqueue(s)eR' = 2N (2) KCRC) Ro ) ~~~According to Nyquist stability criterion, (6) and (7) indicateC2 ~~~~that the closed-loop system is asymptotically stable.2N III. ALGORITHM PROPOSITIONof TCP load N should have a direct impact on stability, In this section we develop an algorithm for tuning REDtransient response and steady-state performance. A small TCP591Authorized licensed use limited to: LOUGHBOROUGH UNIVERSITY. Downloaded on January 23, 2009 at 11:08 from IEEE Xplore.  Restrictions apply.parameters from control theoretic standpoint. Let's consider a C(t) C;single bottlenecked router in which all flows have the sameround-trip time and packet size. Given initial TCP load No , From (5), it is clear that a larger K means faster responsewe have the initial average queue weight ao and other RED time and vice versa. So we choose the average queue weight aparameters fo tsaizin K to tune according to (8) and (9). When TCP load Nparameters from (3) to stabilize the system. Then the beo slagralreracnipvetnintepnefollowing algorithm describes how we change a according to wekee system able. Onthe rotehandwentNecomesthe observedloadN- . ~~~~while keep system stable. On the other hand, when Nbecomesthe observed load N- . smaller, although a smaller o could damp transient response, it(N-)3 makes system retain enough stability margin.No IV. SIMULATIONSwhereNhere < R+C12 We verify our propositions via a set of ns simulations.N0,N .<R+C12 However, our proposition is based on a liberalized modelR(t) - Ro; while ns simulation is actually non-linear in nature.C(t) C; Our aim is to analysis how the change of load level would(N- N2 affect the RED performance and how tuning average queueor af= N ao (9) weight correspondingly could help. Our experiment includes0 / the following steps.where 1. We define a set of parameters in Mode 1, whichNo,N- 2 R+C/2 make the network perform stably, compared withR(t) Ro; network with default RED parameters.C(t) C. 2. We increase the load level, and remain the defaultProof: parameters to analysis the load level's influence.From (1) we have After that, retaining the high load level, we tunea = l- e-K - (1- K8) = K8 the average queue weight according to oura proposition.or K = - (10) 3. We define another set of parameters to make'i equation (3) only satisfied with a large load levelwhere Kg << I in Mode 2, and then reduce the load level to see itsFrom (10) and (3), it follows effect to the queue stability. After that, we reduce°L < (2N)3- (11) the average queue weight correspondingly to see8 red (R C)3 g the output.Given any NO ,N- . R+ C /2 , from ( 1) and (4) we have We look at a single bottle-link router with RED application.The topology of the network is shown in Fig. 2.A number of HTTP flows is also included in this-Lre<( N )3*-08 (12) simulation. The short-lived effects of HTTP flows could be8 dR+C CThus, when N- changes, we have the following algorithm Propagetion delays for the flowsof octhat can satisfy (12) S1 rangeuniformlyaround200ms D1a=KN- a (13)o=N 0f0o S D22where Rt-o tC(t) C;Similarly, given any NO N > R+C /2, we have ' Dataflow SourbeU: Dataflow Des inatibn-~ O:Router with RED)2~~~ ~~~~Sn Q-B- Bofflolink na L < (N-)2 0.1 Sf D8ae (R C)3 C Fig. 2. Network topologyand OG=KCN )aof considered as the noise to the RED queue.where592Authorized licensed use limited to: LOUGHBOROUGH UNIVERSITY. Downloaded on January 23, 2009 at 11:08 from IEEE Xplore.  Restrictions apply.Mode 1: Set parameters: N =300, R = 200 ins, packet size The results are plotted in Fig. 4. We could observe that the500 Bytes, so C =15 MVb/s =3750 packets/s, pmax 0. 1. system goes stable after a certain time to settle to the operatingFrom (4), we have point. After 200 seconds, the current queue length is settledr2N-1I around 475 packets with a small fluctuation.cog = O.lIminI R~CR K04For K 0.01, from (3), we haveCog(2N)red - 3 41 2L K(R C)3In this case, we set Lre tobe0.0002.From (1), we have the minimum dynamic range of thethreshold (maxth - minth) should be 500 packets.A. Experiment 1In the first experiment, we look at a queue with 300 ftp(greedy) and 150 http flows. The bottle link bandwidth is15M\/b/s, and the propagation delays for the flows rangeuniformly between 160 and 240 ins. The minimum thresholdand maximum threshold are set as 150 and 250 packets, withaverage packet size being 500 Bytes. The queue has an 8001packets buffer, and all of the other parameters are set to be C 40 0 86default.We set the RED parameter, gentle_ as "TURE" to make the Fig. 4. N2=300, U2=2.66e-6average queue change smoothly. The current queue length andaverage queue length are shown in Fig. 3. C. Experiment 3As we can see, the average queue length is oscillating In this exper-iment, we add another 100 FTP and 50 HTTParound 250, and the current queue length frequently goes flows into the load level. The pma,x and a remain to the same asdown to zero. It certainly leads to the under-utilization of the in Experiment 2.bottle-link, and brings considerable jitter to the round trip The results are plotted in Fig. 5.time.400~~~~~~~~~~~~~~~~~~~~2OFig. 3. N1~~~300 The system~ ges talei 400 seonswhc isalosta0.,ad Gt 2.6e-6 D. Experiment 43=40,U3=.6eFi.3.N =30Tli, z-zi,m c-fz-znli n 0 z-nnk xviP593nm ,, ,Authorized licensed use limited to: LOUGHBOROUGH UNIVERSITY. Downloaded on January 23, 2009 at 11:08 from IEEE Xplore.  Restrictions apply.Now we increase the average queue weight to maintain a F_2N 1 0constant o/(N)3 ratio in the scale (NO, R+C/2) and a constant cog = 0.lmin 0.133u/(N)2 ratio when N is larger than R+C/2. The results are L(R ) CR JFrom (14), we havecj fi1nt Q4i¢e I ri te (PhfQO iL6gth (2 ) 22 . 3 W r r~~~~~~~~~~~~og 2NK<.( (2) -0.63(R+C)3 Lr ._g ___ red>t- --3--------~--b-__0=u_ So, from (10), we havea-K5<1.69e-5Obviously, a 3.99e -4 is not in this scale.Cu,rreu1t Qu6i1tL1I1 AviiQueutyL6tlTif-4 1111 ffi--. '. Fig 6. N i00 o4. 1e*-6Iplottedin Fig.r 6.tha200- --= -0+t~~~~ * rL .9 -w____J - --LL - -r______M-Xt.ii1 e3~E.~~~~~~~~~~~~~~~~~~~~~~~~ Exeiethi 'LI1Fig. 6. N4=400,a 4=5.9l e-6 -plotted in Fig. 6. It .i o t theCompared with previous outputs, the system res Afeeponsetime i r sb o the lare .indeed goes down. The system performance gets stable in less Hwe when ther afthan2rs ernr00seconds. qa MD 1 200E. Experiment S i lUlic¢ (secoildsLTo analysis how a small load level could affect the system, a lower load lev swe let the load level vary during the simulation to make thecompare more obvious. This simulation starts with 300 FTP The results are plotted in Fig. 7. It is observed that theand 150 HTTP. We set the duration to be 1200 seconds. After response time is extremely small, because of the large oc.one third of the duration, three forth of the data sources stop, However, when there are only 100 FTP flows, the cuffrentand re-start after another 600 seconds. queue length fluctuates between a much larger scale about theBased on Mode 1, we now define Mode 2 that only satisfy average queue. It shows that a lower load level brings a largerequation (3) when the load level is heavy. oscillation range to the queue length. This is because when Nreduces, the right side of the equation (14) gets smaller asMode 2: Firstly, when N-= 300, R = 200 ms, C = 3750 well, that directly make the equation unsatisfied.packets/s, Pmax= 0.1, (maxth- minth)= 500, from (1), we have F Experiment 6L - Pmax - 0.0002 Based on Experiment 5, we now decrease the average queuemax th - minth weight to retain a constant o/(N)3 ratio.From (3) and (4), we have As ao = 3.99e-4, No 300, N 100-cog(2N 2 From (13),wehave(R+C)3Lred a4a=L j ao 1.48e-5(14) No)So we set K=1.5, from (10), we havea = K5 = 3.99e -4 The results are plotted in Fig. 8.Based on our proposition, this average queue weight could We observe the system is settled to stable in 80 seconds,which is a longer duration than it was in Experiment 5.only guarantee a stable RED supporting largeN, because if Hoer,tesclain rudthavagqeewiewe set N down to 100, from (4), we have system is stable obviously gets smaller, which leads a bettersystem performance while the load level reduces.594Authorized licensed use limited to: LOUGHBOROUGH UNIVERSITY. Downloaded on January 23, 2009 at 11:08 from IEEE Xplore.  Restrictions apply.[9] M. May, J. Bolot, C. Diot and B. Lyles, "Reasons not to deploy RED",in Proceedings of 7th International Workshop on Quality of Service,(ULrrtau ¢tlelj~ \r~u~fin279@m8e ¢t' } ULondon, 31 May-4 June 1999, pp. 260-262.[10] D. Katabi and C. Blake, "A note on the stability requirements ofadaptive virtual queue", LCS Document Number: MIT-LCS-TM-626, 2-13-2002. [Online] Available:htty://www.1cs.mit.edu/yu,blications/pubs/pdf/MIT-LCS-TM-626 f.[11] S. Floyd, R. Gummadi and S. Shenker, "Adaptive RED: an algorithm for_ ._.J_ _ _ _ ___ _ _ _ _ __ _ _ _ _ _ _. __ _ _ _ __ __ _ _ _ _ _ increasing the robustness of RED's active queue management." [Online]Available: http://www.icir.org/floyd/papers/adaptiveRed.pdf, Aug. 2001.--------a2- 4t1l____'___ *1g'| iStj 3miae O&OIFig. 8: N6=300-100, 6=1.48e-5V. CONCLUSIONWe have developed a simple, scalable rule of parametertuning for RED routers by performing a control-theoreticanalysis of TCP/RED system. Instead of tuning manyparameters of RED, we adapt only average queue weight tothe changing network loads while the TCP/RED system keepstable and achieves desirable transient performance. Thesimulation results verified our analysis. This is a starting pointfor decoupling RED parameters. In practice, there are morecomplex and realistic network conditions under which weneed to explore. We are currently studying the self-tuningalgorithm for RED to enhance its performance in practicalnetworks involving flows with variable round-trip time andpacket size.REFERENCES[1] Vishal Misra, Wei-Bo Gong, and Don Towsley, "Fluid-based Analysisof a Network ofAQM Routers Supporting TCP Flows with anApplication to RED," in Proceedings of ACM/SIGCOMM, 2000.[2] C. Hollot, V. Misra, D. Towlsey, and W. Gong, "A control theoreticanalysis of red," in the proceedings of IEEE Infocom 2001,Anchorage,Alaska, Apr. 2001.[3] 5. Floyd and V. Jacobson, "Random Early Detection Gateways forCongestion Avoidance," IEEE/ACM Transactions on Networking, vol.1, no. 4, pp. 397-413, Aug. 1993.[4] Wu-chang Feng, Kang G. Shin, Dilip D. Kandlur, Debanjan Saha"BLUE Active Queue Management Algorithms," IEEE/ACMTransactions on Networking, Volume 10, Issue 4, August 2002.[5] Bartek Wydrowski and Moshe Zukerman, "GREEN: An Active QueueManagement Algorithm for a Self Managed Internet," Proceedings ofICC 2002, New York, vol. 4, pp. 2368-2372, 2002.[6] 5. Athuraliya, V. H. Li, S. H. Low, and Q. Yin, "REM: Active QueueManagement," IEEE Network, vol. 15, no. 3, pp 48-53, May 2001.[7 M. Chitasn. Jefy .Ot an F. D.Smih Tnn E oSween 200 pae 139-150 1.dmanagement~~~~~~~~~~~~~~~~~I WtonWepefrac" in Proceig of SIGCOMM',03--, - __Kalsuh,Gemay,Ag. 2003, pp 265-276>-1S ;M1>6- f~~59Authorized licensed use limited to: LOUGHBOROUGH UNIVERSITY. Downloaded on January 23, 2009 at 11:08 from IEEE Xplore.  Restrictions apply.

An average queue weight parameterization in a network supporting TCP flows with RED

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An average queue weight parameterization in a network supporting TCP flows with RED

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