UDP. The long-lived TCP flows and unresponsive UDP flows are active at all times during the simulation. The UDP flows are characterised by a constant bit rate.
A Comparative Study of Active Queue Management Schemes Arkaitz Bitorika, Mathieu Robin, Meriel Huggard, Ciar´an Mc Goldrick Department of Computer Science Trinity College Dublin, Ireland Email: {bitorika, robinm, huggardm, cmcgldrk}@cs.tcd.ie
Abstract— Active Queue Management (AQM) schemes are a class of queueing algorithms designed to surmount some of the shortcomings of classic Drop-Tail queues in best-effort networks. Much recent work has focused on improving AQM performance through alternate approaches: in excess of 50 schemes have been proposed since 1999 alone. This study details a simulationbased evaluation and comparison of a subset of these schemes. It utilises a specially designed framework which builds on the NS 2 simulator. The schemes chosen have been designed to allow for implementation and incremental deployment on routers of the existing Internet architecture. The evaluation methodology adopted enables the direct comparison of AQM schemes, clearly highlighting their similarities and differences. It thus addresses the current need for an unbiased comparative evaluation of the various proposals. Such work should encourage faster adoption of improved AQM algorithms in Internet routers.
I. I NTRODUCTION Active Queue Management denotes a class of algorithms designed to provide improved queueing mechanisms for routers. Research in this area was inspired by the original RED proposal [1] in 1993. These schemes are called active because they dynamically signal congestion to sources; either explicitly, by marking packets (e.g. Explicit Congestion Notification [2]) or implicitly, by dropping packets. This is in contrast to Drop-Tail queueing which is passive: packets are dropped if, and only if, the queue is full. The Internet Engineering Task Force (IETF) recommended the deployment of AQM in Internet routers in 1998. The main motivations given were the improvement of performance and the prevention of congestion collapse which may arise from the growth of non-responsive traffic on the Internet [3]. Since this IETF recommendation, the main focus of the research community has been on the development of new AQM schemes. In a recent study [4], it was noted that more than 50 new algorithms have been proposed since 1999. It is hard to estimate the state of AQM deployment today, as there is no tractable way to probe routers to determine if they implement some form of AQM. Moreover, ISP deployment information is commercially sensitive. While RED (or some vendor-specific variant of RED, e.g. Cisco’s WRED [5]) is available in most routers today, this is often disabled by default. It should be noted that RED has a number of drawbacks and, indeed, some even recommend against its deployment [6]. Almost all schemes addressed in [4] are presented as improvements on RED. While RED has clearly evolved from
the original proposal (there are now gentle and adaptive [7] versions), the other schemes propose alternate approaches to queue management in router buffers. Most AQM evaluation work has been carried out for the purpose of validating new schemes. Due to the differences in both models and design goals of the various schemes, a wide range of network scenarios and performance metrics have been used in the literature to evaluate and compare AQM schemes. The challenge is to evaluate the various schemes proposed in a consistent and unbiased fashion. While some independent studies exist [8], [9], additional work is needed to evaluate recent proposals (73% of the schemes studied in [4] have been published since 2000). In this paper eight AQM schemes are selected for detailed evaluation. The main criterion used for selection of these schemes is the ease with which they may be deployed in existing best-effort networks. The evaluation is carried out using a specially developed framework [10] which uses the NS 2 simulator [11]. A consistent evaluation of schemes using the chosen performance metrics facilitates an unbiased comparison which highlights their similarities and differences. The paper is organised as follows: the criteria used to select schemes for this study, together with an outline of schemes chosen, is given in section II. Section III provides a short overview of the simulation and evaluation methodologies, including the metrics and the network scenarios used. The results obtained are presented and analysed in Section IV. Finally, this paper concludes with an overview of the results obtained and a discussion on how the results could be extended in Section V. II. S COPE OF THE S TUDY A. Selection Methodology In excess of 50 schemes with a wide variety of goals and parameterisations cannot be meaningfully compared in a short study; the schemes studied in this paper were selected according to the following criteria: • The schemes can be deployed directly, and incrementally, in place of current router implementations, without the need to modify Internet protocols on the end nodes. • The schemes must use no per flow tracking, to allow for use with high-bandwidth core routers. • The algorithms must be adequately documented in the peer-reviewed literature.
Only schemes targeted at best-effort IP networks are considered. • For evolutionary schemes the most recently available version is considered. This list represents some of the key criteria needed to successfully deploy an AQM scheme. A selection of AQM schemes that satisfy these criteria, and thus may be easily deployed in best-effort IP networks, is presented below. •
B. Chosen AQM Schemes Applying these requirements to the list of schemes in [4] resulted in the selection of eight algorithms for this study. These are presented in chronological order of publication in table I. Each scheme has been characterised according to its design philosophy, congestion metric and main goal. It should be noted that “Performance” in the goal column of the table denotes an algorithm designed to improve overall network performance. Also, LDC considers both queue length and traffic load as congestion metrics. ARED (Adaptive RED) has been selected as it is the latest version of RED, and thus is likely to supersede previous versions of RED (such as WRED). AQM Scheme ARED [12] REM [13] CHOKe [14] PI [15] AVQ [16] DRED [17] GREEN [18] LDC [19]
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C. Parameter settings One of the major difficulties encountered was in ensuring the consistency and the fairness of this evaluation. An average of five parameters [10] are required for each scheme, and each one influences the algorithm’s performance. By way of example, a recent study [20] has shown that the choice of the queue unit for RED (bytes or packets) can have a significant impact on its performance. Much criticism of the original RED proposal stemmed from the parameterisation guidelines provided, as these were not always appropriate for a wide range of network scenarios [6], [7], [21]. In the schemes under consideration, most authors provide parameterisation guidelines. However, these are not always complete and, in some cases, must be inferred from the evaluation scenarios described in the respective AQM proposal. Some guidelines assume a priori knowledge of the traffic model and characteristics (e.g. mean packet size for RED in byte mode). Routers that use these algorithms may experience problems when faced with unpredictable Internet traffic that fails to conform to the adopted model.
III. M ETHODOLOGY A. Simulation Methodology The eight algorithms presented in the previous section were evaluated with the simulation framework described in [10]. This framework has been developed to provide for the evaluation of AQM schemes, through a powerful user interface to NS 2. Its Python API allows the user to set up a large number of NS 2 simulations, and to aggregate and summarise the results. The framework can improve the statistical significance of the results by the appropriate processing of output data. In doing so, it removes the initial transient using the Marginal Confidence Rule described in [22], and determines the simulation run-length dynamically. To achieve a given level of confidence, it also computes the number of replications for one simulation dynamically by employing the replication/deletion approach [23]. The results presented in this paper were obtained using a relative confidence interval of, at most, ±15% of the mean, with 95% confidence. The results described in Section IV were obtained using the latest version, 2.26, of the NS 2 simulator. The pseudo-random number generator built into NS 2 (the ´ combined multiple recursive generator of L’Ecuyer [24]) is used at all times. The framework provides five main metrics: drop, utilisation, fairness, delay and jitter. Their precise definition and implementation, together with the rational for their choice, is described in [10]. Queue length distributions are also included to facilitate detailed examination of the AQM scheme dynamics. B. Network Scenarios A network scenario is defined as the combination of a traffic mix and a network topology. The traffic is a mix of longlived TCP flows (e.g. FTP transfers of large files), short-lived TCP flows (e.g. Web traffic), and unresponsive flows using UDP. The long-lived TCP flows and unresponsive UDP flows are active at all times during the simulation. The UDP flows are characterised by a constant bit rate. The model for shortlived traffic is more complex: a traffic generator built into NS 2 is used and the parameters of this model are specified using measurement data from [25]. In particular, it was found that the distribution of the web server response size (in bytes) is well fitted by a Pareto-II distribution, with shape parameter α = 0.97 and scale parameter β = 838B. Note that the inter-page time is set to be almost zero: while this approach is not strictly accurate, it allows for a better comparison of the fairness of algorithms. Like long-lived TCP and UDP flows, the shortlived TCP flows are active throughout the simulation: a fair algorithm would allocate the same resources to any flow that is active. Two different topologies were used for evaluating the algorithms, and these are described below: 1) Dumbbell: The dumbbell topology denotes a network composed of a single link which is congested with one-way traffic (see figure 1). The framework allows different roundtrip times (RTT) for each traffic flow. The bounds of this distribution are taken from Internet measurements [26].
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In this section, the stability of the AQM-controlled queue length is studied in the case where the network traffic is responsive. Maintaining queue stability is important as some applications are sensitive to jitter. Furthermore, the queue occupation level should ideally be as low as possible, to ensure low delay. However the queue should never be empty, to ensure maximum utilisation of the outgoing link. The dumbbell scenario described in section III-B is used, with a mix of 50 long-lived and 50 short-lived TCP flows. Using the specifications above, the maximum queue size is set to 125 packets, and the target delay queue length to 25 packets for the algorithms which support this parameter (i.e. PI, DRED, and REM). The queue length distribution for the various algorithms is shown in figure 3. It should be noted that the vertical axis scaling is different for each graph in figure 3. The cumulative distribution function of the eight algorithms, along with the results for Drop-Tail, are given in figure 4. ARED
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2) Reversebell: It has been pointed out that the Dumbbell topology has limitations [27]. Thus it is inappropriate that it be the only model used. The reversebell scenario is more realistic and complex: it includes reverse-path traffic and multiple congested links. Figure 2 gives an example of a Reversebell scenario. Its topology consists of a high-bandwidth 1 Gb/s core link with a set of lower-bandwidth 45 Mb/s links attached to it and sources connected by 100 Mb/s links. In this scenario, the congestion occurs typically in the 45 Mb/s links between the high-bandwidth core and the LAN-like traffic source links. This scenario provides a more realistic network topology in which the core is over-provisioned and congestion occurs on more peripheral links. The presence of traffic on both directions of a link and flows traversing more than one congested queue makes this topology more realistic. Similar topologies are discussed, and compared with dumbbell scenarios, in [27].
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The parameters are set according to the guidelines provided within each scheme proposal. ECN [2] is not used, as some of the schemes considered here do not support it and, furthermore, its deployment is limited [20]. The queue buffer size is always set to the number of packets needed to fill the outgoing link for 50 ms; this provides a reasonable upper-bound on the queueing delay. Where possible, the queueing algorithms are configured to have a target delay of 10 ms. These values are computed by taking into account the outgoing link they are attached to and by supposing a mean packet size of 500 bytes. Using these guidelines for parameter settings, the schemes are now evaluated using the following three criteria: queue length stability, fairness, and network performance.
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Based on these results, several observations may be made. The algorithms appear to be split into three categories: • Amongst the algorithms specifically designed to keep the queue length around a given target, a clear hierarchy ap-
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pears. DRED performs better than PI, while PI performs better than REM. Note that there is no clearly defined guidelines for REM parameters, so this may explain the poor performance of this algorithm in this scenario. • The second category represents algorithms whose queue length distribution appears to be exponential in this experimental scenario. These are ARED, AVQ, LDC, and CHOKe (the leftmost column of figure 3). ARED exhibits similar behaviour to AVQ. However, AVQ maintains a smaller queue length, and also performs better than ARED at maintaining a non-empty queue. The queue length is especially low for LDC and CHOKe (in fact it is almost always zero). The behaviour of these two algorithms is analysed in more detail in section IV-C. • GREEN behaves differently to the other algorithms. As shown in figure 4, its behaviour is closest to that of DropTail, except that the queue is empty or small some of the time (while the Drop-Tail queue is always full). This may be attributed to the fact that GREEN’s only congestion metric is traffic load; it doesn’t keep track of the queue length. Thus it can reach a steady-state where the queue is full, while keeping the incoming traffic rate close to the target. Overall, two of the algorithms considered seem to achieve better performance: DRED for keeping the queue very close to the target queue length, and AVQ for keeping the queue length small but non-zero. B. Fairness One of the goals of AQM is to improve on the fairness of Drop-Tail queueing. RFC 2309 [3] highlighted the need for router queueing mechanisms to protect responsive flows from other non-responsive or less responsive traffic Some AQM proposals try to provide this protection (e.g. CHOKe). In this study, Jain’s fairness index [28] is applied to throughput of individual flows and is the main fairness metric used. The effectiveness of the AQM schemes at protecting TCP
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traffic from non-responsive UDP flows will be evaluated in a scenario where constant-bitrate UDP traffic is introduced to compete with existing FTP flows. Figure 5 shows that most schemes exhibit similar fairness as the UDP traffic is introduced, with the fairness of all schemes decreasing as the UDP traffic load increases. CHOKe is least fair when UDP rates are low but performs much better than the other schemes when the offered UDP rate exceeds 50% of the bottleneck capacity. LDC has the best fairness when no UDP traffic is present but its fairness is worse than that of the other schemes once UDP traffic is introduced. 0.4
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TCP flows that are short-lived are more sensitive to packet drops than long-lived FTP-like flows. This may be studied using a scenario consisting of a base traffic mix of short-lived HTTP flows, with the introduction of more aggressive FTP flows, as seen in figure 6. In this scenario, CHOKe provides the best fairness, with the remaining schemes having lower
fairness, particularly AVQ which exhibits poor fairness for all traffic mixes. For all schemes, the introduction of long-lived TCP traffic does not significantly affect the fairness achieved. Another observation drawn from figures 5 and 6 is that the schemes studied are able to achieve considerably better fairness, φ, when the traffic is composed only of long-lived TCP flows (figure 5, φ ≈ 0.7), than when it is purely composed of short-lived flows (figure 6, φ ≈ 0.25). C. Performance The global performance of the AQM schemes is evaluated in this section. The simulation model used is the reversebell scenario described in section III-B. This scenario has been chosen because it captures more of the characteristics of the Internet than a simple dumbbell. 0.9
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while the delay is the second lowest. LDC exhibits the lowest queueing delay of all schemes, however the utilisation is also the lowest. As noted in section IV-A, the LDC queue is empty most of the time. This could be explained by a fundamental aspect of the LDC design: the drop probability is high if the considered congestion metrics (i.e. queue length and traffic load) are close to their respective targets. Thus the value obtained is much below the target utilisation. GREEN stands out as having the highest queueing delay: the only congestion metric it considers is the incoming traffic load and, as the queue size is not controlled, this may lead to situations where it behaves similarly to a Drop-Tail queue. While the other algorithms do not differ significantly, it should be noted that the utilisation achieved by CHOKe is lower. This may be explained by the fact that CHOKe drops more packets to ensure better fairness.
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Performance of AQM schemes may be defined as how good the queueing discipline is at achieving low queueing delay and good fairness, while maintaining high utilisation of the outgoing link. Figure 7 illustrates the delay / utilisation tradeoff obtained for the chosen schemes. AVQ clearly achieves the best performance in this experiment: it provides the highest utilisation of all the schemes,
In this paper, the performance of eight AQM schemes, selected from amongst the many published over the past three years, has been evaluated. The main goals of AQM are to enhance network performance and ensure fairness, especially in the presence of unresponsive flows. It may be argued that none of the algorithms considered perform well on both these aspects for the scenarios considered. AVQ achieves good utilisation while keeping the queue length small. On the other hand, CHOKe provides much better fairness, but fails to keep the utilisation as high as AVQ. In this study two of the algorithms did not perform very well: GREEN behaved similarly to Drop-Tail, while LDC was too aggressive at dropping packets. PI and DRED appeared to be good at controlling the queue length. PI, DRED, ARED and REM all exhibit good network performance, however the differences observed are not significant enough to really distinguish these from each other. To perform this study, it was necessary to restrict the number of algorithms considered. The criteria used for the selection process were, of necessity, subjective in nature. However, this should not undermine the validity of the subsequent performance analysis and comparative study. It may also be noted that the parameter settings recommended for each scheme have been used and, hence, the performance of some of the schemes may be improved by using different parameter settings. ARED has benefited from discussions in the literature on the settings of parameters for RED [6], [29], whilst other algorithms have received much less attention. The evaluation methodology described herein could easily be used to provide an extended, detailed evaluation and comparison of a much larger set of AQM schemes. Moreover, the number of performance metrics used could be increased, the selection criteria relaxed, and the simulation scenarios made more diverse and realistic. Such a study would contribute greatly to network research by enhancing the understanding of existing schemes, thus providing a strong foundation for future AQM research, and hence, facilitating the future deployment of AQM throughout the Internet.
ACKNOWLEDGEMENTS The authors wish to thank Vishal Misra and Christopher V. Hollot for helpful correspondence about the PI algorithm. This work has been supported by Enterprise Ireland Basic Research Grant, SC/2002/293. R EFERENCES [1] S. Floyd and V. Jacobson, “Random early detection gateways for congestion avoidance,” IEEE/ACM Trans. Networking, vol. 1, no. 4, pp. 397–413, Aug. 1993. [2] S. Floyd, “TCP and explicit congestion notification,” ACM Computer Communication Review, vol. 24, no. 5, pp. 10–23, 1994. [3] B. Braden et al., “Recommendations on queue management and congestion avoidance in the Internet,” RFC 2309, Apr. 1998. [4] A. Bitorika, M. Robin, and M. Huggard, “A survey of active queue management schemes,” Trinity College Dublin, Department of Computer Science, Tech. Rep., Sept. 2003. [5] Cisco Systems, “Weighted random early detection on the Cisco 12000 series router,” Mar. 2002. [6] M. May, J. Bolot, C. Diot, and B. Lyles, “Reasons not to deploy RED,” in Proc. IWQoS’99, June 1999, pp. 260–262. [7] W. chang Feng, D. D. Kandlur, D. Saha, and K. G. Shin, “A self-configuring RED gateway,” in Proc. INFOCOM’99, vol. 3. IEEE, Mar. 1999, pp. 1320–1328. [8] C. Zhu, O. Yang, J. Aweya, M. Ouellette, and D. Montuno, “A comparison of active queue management algorithms using the OPNET modeler,” IEEE Commun. Mag., vol. 40, no. 6, pp. 158–167, June 2002. [9] G. Iannaccone, C. Brandauer, T. Ziegler, C. Diot, S. Fdida, and M. May, “Comparison of tail drop and active queue management performance for bulk-data and web-like internet traffic,” in Proc. ISCC. IEEE, July 2001, pp. 122–129. [10] A. Bitorika, M. Robin, and M. Huggard, “An evaluation framework for active queue management schemes,” in Proc. MASCOTS’03. IEEE, Oct. 2003, to appear. [11] L. Breslau, D. Estrin, K. Fall, S. Floyd, J. Heidemann, A. Helmy, P. Huang, S. McCanne, K. Varadhan, Y. Xu, and H. Yu, “Advances in network simulation,” IEEE Computer, vol. 33, no. 5, pp. 59–67, May 2000. [12] S. Floyd, R. Gummadi, and S. Shenker, “Adaptive RED: An algorithm for increasing the robustness of RED,” Aug. 2001, under submission. [13] S. Athuraliya, D. E. Lapsley, and S. H. Low, “An enhanced random early marking algorithm for internet flow control,” in Proc. INFOCOM’00, vol. 3. IEEE, Mar. 2000.
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