RFC 7928
Characterization Guidelines for Active Queue Management (AQM)
Pages: 37
Informational
Informational
Part 2 of 2 – Pages 18 to 37
5. Transport Protocols
Network and end-devices need to be configured with a reasonable amount of buffer space to absorb transient bursts. In some situations, network providers tend to configure devices with large buffers to avoid packet drops triggered by a full buffer and to maximize the link utilization for standard loss-based TCP traffic. AQM algorithms are often evaluated by considering the Transmission Control Protocol (TCP) [RFC793] with a limited number of applications. TCP is a widely deployed transport. It fills up available buffers until a sender transferring a bulk flow with TCP receives a signal (packet drop) that reduces the sending rate. The larger the buffer, the higher the buffer occupancy, and therefore the queuing delay. An efficient AQM scheme sends out early congestion signals to TCP to bring the queuing delay under control. Not all endpoints (or applications) using TCP use the same flavor of TCP. A variety of senders generate different classes of traffic, which may not react to congestion signals (aka non-responsive flows in Section 3 of the AQM recommendation document [RFC7567]) or may not reduce their sending rate as expected (aka Transport Flows that are less responsive than TCP, such as proposed in Section 3 of the AQM recommendation document [RFC7567], also called "aggressive flows"). In these cases, AQM schemes seek to control the queuing delay. This section provides guidelines to assess the performance of an AQM proposal for various traffic profiles -- different types of senders (with different TCP congestion control variants, unresponsive, and aggressive).
5.1. TCP-Friendly Sender
5.1.1. TCP-Friendly Sender with the Same Initial Congestion Window
This scenario helps to evaluate how an AQM scheme reacts to a TCP- friendly transport sender. A single, long-lived, non-application- limited, TCP NewReno flow, with an Initial congestion Window (IW) set to 3 packets, transfers data between sender A and receiver B. Other TCP-friendly congestion control schemes such as TCP-Friendly Rate Control [RFC5348], etc., may also be considered. For each TCP-friendly transport considered, the graph described in Section 2.7 could be generated.5.1.2. TCP-Friendly Sender with Different Initial Congestion Windows
This scenario can be used to evaluate how an AQM scheme adapts to a traffic mix consisting of TCP flows with different values of the IW. For this scenario, two types of flows must be generated between sender A and receiver B: o A single, long-lived non-application-limited TCP NewReno flow; o A single, application-limited TCP NewReno flow, with an IW set to 3 or 10 packets. The size of the data transferred must be strictly higher than 10 packets and should be lower than 100 packets. The transmission of the non-application-limited flow must start first and the transmission of the application-limited flow starts after the non-application-limited flow has reached steady state. The steady state can be assumed when the goodput is stable. For each of these scenarios, the graph described in Section 2.7 could be generated for each class of traffic (application-limited and non- application-limited). The completion time of the application-limited TCP flow could be measured.5.2. Aggressive Transport Sender
This scenario helps testers to evaluate how an AQM scheme reacts to a transport sender that is more aggressive than a single TCP-friendly sender. We define 'aggressiveness' as a higher-than-standard increase factor upon a successful transmission and/or a lower-than- standard decrease factor upon a unsuccessful transmission (e.g., in case of congestion controls with the Additive Increase Multiplicative Decrease (AIMD) principle, a larger AI and/or MD factors). A single
long-lived, non-application-limited, CUBIC flow transfers data between sender A and receiver B. Other aggressive congestion control schemes may also be considered. For each flavor of aggressive transports, the graph described in Section 2.7 could be generated.5.3. Unresponsive Transport Sender
This scenario helps testers evaluate how an AQM scheme reacts to a transport sender that is less responsive than TCP. Note that faulty transport implementations on an end host and/or faulty network elements en route that "hide" congestion signals in packet headers may also lead to a similar situation, such that the AQM scheme needs to adapt to unresponsive traffic (see Section 3 of the AQM recommendation document [RFC7567]). To this end, these guidelines propose the two following scenarios: o The first scenario can be used to evaluate queue build up. It considers unresponsive flow(s) whose sending rate is greater than the bottleneck link capacity between routers L and R. This scenario consists of a long-lived non-application-limited UDP flow that transmits data between sender A and receiver B. The graph described in Section 2.7 could be generated. o The second scenario can be used to evaluate if the AQM scheme is able to keep the responsive fraction under control. This scenario considers a mixture of TCP-friendly and unresponsive traffic. It consists of a long-lived UDP flow from unresponsive application and a single long-lived, non-application-limited (unlimited data available to the transport sender from the application layer), TCP New Reno flow that transmit data between sender A and receiver B. As opposed to the first scenario, the rate of the UDP traffic should not be greater than the bottleneck capacity, and should be higher than half of the bottleneck capacity. For each type of traffic, the graph described in Section 2.7 could be generated.5.4. Less-than-Best-Effort Transport Sender
This scenario helps to evaluate how an AQM scheme reacts to LBE congestion control that "results in smaller bandwidth and/or delay impact on standard TCP than standard TCP itself, when sharing a bottleneck with it" [RFC6297]. There are potential fateful interactions when AQM and LBE techniques are combined [GONG2014]; this scenario helps to evaluate whether the coexistence of the proposed AQM and LBE techniques may be possible.
A single long-lived non-application-limited TCP NewReno flow transfers data between sender A and receiver B. Other TCP-friendly congestion control schemes may also be considered. Single long-lived non-application-limited LEDBAT [RFC6817] flows transfer data between sender A and receiver B. We recommend setting the target delay and gain values of LEDBAT to 5 ms and 10, respectively [TRAN2014]. Other LBE congestion control schemes may also be considered and are listed in the IETF survey of LBE protocols [RFC6297]. For each of the TCP-friendly and LBE transports, the graph described in Section 2.7 could be generated.6. Round-Trip Time Fairness
6.1. Motivation
An AQM scheme's congestion signals (via drops or ECN marks) must reach the transport sender so that a responsive sender can initiate its congestion control mechanism and adjust the sending rate. This procedure is thus dependent on the end-to-end path RTT. When the RTT varies, the onset of congestion control is impacted, and in turn impacts the ability of an AQM scheme to control the queue. It is therefore important to assess the AQM schemes for a set of RTTs between A and B (e.g., from 5 to 200 ms). The asymmetry in terms of difference in intrinsic RTT between various paths sharing the same bottleneck should be considered, so that the fairness between the flows can be discussed. In this scenario, a flow traversing on a shorter RTT path may react faster to congestion and recover faster from it compared to another flow on a longer RTT path. The introduction of AQM schemes may potentially improve the RTT fairness. Introducing an AQM scheme may cause unfairness between the flows, even if the RTTs are identical. This potential unfairness should be investigated as well.6.2. Recommended Tests
The recommended topology is detailed in Figure 1. To evaluate the RTT fairness, for each run, two flows are divided into two categories. Category I whose RTT between sender A and receiver B should be 100 ms. Category II, in which the RTT between sender A and receiver B should be in the range [5 ms, 560 ms] inclusive. The maximum value for the RTT represents the RTT of a satellite link [RFC2488].
A set of evaluated flows must use the same congestion control algorithm: all the generated flows could be single long-lived non- application-limited TCP NewReno flows.6.3. Metrics to Evaluate the RTT Fairness
The outputs that must be measured are: (1) the cumulative average goodput of the flow from Category I, goodput_Cat_I (see Section 2.5 for the estimation of the goodput); (2) the cumulative average goodput of the flow from Category II, goodput_Cat_II (see Section 2.5 for the estimation of the goodput); (3) the ratio goodput_Cat_II/ goodput_Cat_I; and (4) the average packet drop rate for each category (Section 2.3).7. Burst Absorption
"AQM mechanisms might need to control the overall queue sizes to ensure that arriving bursts can be accommodated without dropping packets" [RFC7567].7.1. Motivation
An AQM scheme can face bursts of packet arrivals due to various reasons. Dropping one or more packets from a burst can result in performance penalties for the corresponding flows, since dropped packets have to be retransmitted. Performance penalties can result in failing to meet Service Level Agreements (SLAs) and can be a disincentive to AQM adoption. The ability to accommodate bursts translates to larger queue length and hence more queuing delay. On the one hand, it is important that an AQM scheme quickly brings bursty traffic under control. On the other hand, a peak in the packet drop rates to bring a packet burst quickly under control could result in multiple drops per flow and severely impact transport and application performance. Therefore, an AQM scheme ought to bring bursts under control by balancing both aspects -- (1) queuing delay spikes are minimized and (2) performance penalties for ongoing flows in terms of packet drops are minimized. An AQM scheme that maintains short queues allows some remaining space in the buffer for bursts of arriving packets. The tolerance to bursts of packets depends upon the number of packets in the queue, which is directly linked to the AQM algorithm. Moreover, an AQM scheme may implement a feature controlling the maximum size of accepted bursts that can depend on the buffer occupancy or the currently estimated queuing delay. The impact of the buffer size on the burst allowance may be evaluated.
7.2. Recommended Tests
For this scenario, the tester must evaluate how the AQM performs with a traffic mix. The traffic mix could be composed of (from sender A to receiver B): o Burst of packets at the beginning of a transmission, such as web traffic with IW10; o Applications that send large bursts of data, such as bursty video frames; o Background traffic, such as Constant Bit Rate (CBR) UDP traffic and/or A single non-application-limited bulk TCP flow as background traffic. Figure 2 presents the various cases for the traffic that must be generated between sender A and receiver B. +-------------------------------------------------+ |Case| Traffic Type | | +-----+------------+----+--------------------+ | |Video|Web (IW 10)| CBR| Bulk TCP Traffic | +----|-----|------------|----|--------------------| |I | 0 | 1 | 1 | 0 | +----|-----|------------|----|--------------------| |II | 0 | 1 | 1 | 1 | |----|-----|------------|----|--------------------| |III | 1 | 1 | 1 | 0 | +----|-----|------------|----|--------------------| |IV | 1 | 1 | 1 | 1 | +----+-----+------------+----+--------------------+ Figure 2: Bursty Traffic Scenarios A new web page download could start after the previous web page download is finished. Each web page could be composed of at least 50 objects and the size of each object should be at least 1 KB. Six TCP parallel connections should be generated to download the objects, each parallel connection having an initial congestion window set to 10 packets. For each of these scenarios, the graph described in Section 2.7 could be generated for each application. Metrics such as end-to-end latency, jitter, and flow completion time may be generated. For the cases of frame generation of bursty video traffic as well as the choice of web traffic pattern, these details and their presentation are left to the testers.
8. Stability
8.1. Motivation
The safety of an AQM scheme is directly related to its stability under varying operating conditions such as varying traffic profiles and fluctuating network conditions. Since operating conditions can vary often, the AQM needs to remain stable under these conditions without the need for additional external tuning. Network devices can experience varying operating conditions depending on factors such as time of the day, deployment scenario, etc. For example: o Traffic and congestion levels are higher during peak hours than off-peak hours. o In the presence of a scheduler, the draining rate of a queue can vary depending on the occupancy of other queues: a low load on a high-priority queue implies a higher draining rate for the lower- priority queues. o The capacity available can vary over time (e.g., a lossy channel, a link supporting traffic in a higher Diffserv class). Whether or not the target context is a stable environment, the ability of an AQM scheme to maintain its control over the queuing delay and buffer occupancy can be challenged. This document proposes guidelines to assess the behavior of AQM schemes under varying congestion levels and varying draining rates.8.2. Recommended Tests
Note that the traffic profiles explained below comprises non- application-limited TCP flows. For each of the below scenarios, the graphs described in Section 2.7 should be generated, and the goodput of the various flows should be cumulated. For Section 8.2.5 and Section 8.2.6, they should incorporate the results in a per-phase basis as well. Wherever the notion of time has been explicitly mentioned in this subsection, time 0 starts from the moment all TCP flows have already reached their congestion avoidance phase.
8.2.1. Definition of the Congestion Level
In these guidelines, the congestion levels are represented by the projected packet drop rate, which is determined when there is no AQM scheme (i.e., a drop-tail queue). When the bottleneck is shared among non-application-limited TCP flows, l_r (the loss rate projection) can be expressed as a function of N, the number of bulk TCP flows, and S, the sum of the bandwidth-delay product and the maximum buffer size, both expressed in packets, based on Eq. 3 of [MORR2000]: l_r = 0.76 * N^2 / S^2 N = S * SQRT(1/0.76) * SQRT(l_r) These guidelines use the loss rate to define the different congestion levels, but they do not stipulate that in other circumstances, measuring the congestion level gives you an accurate estimation of the loss rate or vice versa.8.2.2. Mild Congestion
This scenario can be used to evaluate how an AQM scheme reacts to a light load of incoming traffic resulting in mild congestion -- packet drop rates around 0.1%. The number of bulk flows required to achieve this congestion level, N_mild, is then: N_mild = ROUND (0.036*S)8.2.3. Medium Congestion
This scenario can be used to evaluate how an AQM scheme reacts to incoming traffic resulting in medium congestion -- packet drop rates around 0.5%. The number of bulk flows required to achieve this congestion level, N_med, is then: N_med = ROUND (0.081*S)8.2.4. Heavy Congestion
This scenario can be used to evaluate how an AQM scheme reacts to incoming traffic resulting in heavy congestion -- packet drop rates around 1%. The number of bulk flows required to achieve this congestion level, N_heavy, is then: N_heavy = ROUND (0.114*S)
8.2.5. Varying the Congestion Level
This scenario can be used to evaluate how an AQM scheme reacts to incoming traffic resulting in various levels of congestion during the experiment. In this scenario, the congestion level varies within a large timescale. The following phases may be considered: phase I -- mild congestion during 0-20 s; phase II -- medium congestion during 20-40 s; phase III -- heavy congestion during 40-60 s; phase I again, and so on.8.2.6. Varying Available Capacity
This scenario can be used to help characterize how the AQM behaves and adapts to bandwidth changes. The experiments are not meant to reflect the exact conditions of Wi-Fi environments since it is hard to design repetitive experiments or accurate simulations for such scenarios. To emulate varying draining rates, the bottleneck capacity between nodes 'Router L' and 'Router R' varies over the course of the experiment as follows: o Experiment 1: The capacity varies between two values within a large timescale. As an example, the following phases may be considered: phase I -- 100 Mbps during 0-20 s; phase II -- 10 Mbps during 20-40 s; phase I again, and so on. o Experiment 2: The capacity varies between two values within a short timescale. As an example, the following phases may be considered: phase I -- 100 Mbps during 0-100 ms; phase II -- 10 Mbps during 100-200 ms; phase I again, and so on. The tester may choose a phase time-interval value different than what is stated above, if the network's path conditions (such as bandwidth- delay product) necessitate. In this case, the choice of such a time- interval value should be stated and elaborated. The tester may additionally evaluate the two mentioned scenarios (short-term and long-term capacity variations), during and/or including the TCP slow-start phase. More realistic fluctuating capacity patterns may be considered. The tester may choose to incorporate realistic scenarios with regards to common fluctuation of bandwidth in state-of-the-art technologies. The scenario consists of TCP NewReno flows between sender A and receiver B. To better assess the impact of draining rates on the AQM behavior, the tester must compare its performance with those of drop-
tail and should provide a reference document for their proposal discussing performance and deployment compared to those of drop-tail. Burst traffic, such as presented in Section 7.2, could also be considered to assess the impact of varying available capacity on the burst absorption of the AQM.8.3. Parameter Sensitivity and Stability Analysis
The control law used by an AQM is the primary means by which the queuing delay is controlled. Hence, understanding the control law is critical to understanding the behavior of the AQM scheme. The control law could include several input parameters whose values affect the AQM scheme's output behavior and its stability. Additionally, AQM schemes may auto-tune parameter values in order to maintain stability under different network conditions (such as different congestion levels, draining rates, or network environments). The stability of these auto-tuning techniques is also important to understand. Transports operating under the control of AQM experience the effect of multiple control loops that react over different timescales. It is therefore important that proposed AQM schemes are seen to be stable when they are deployed at multiple points of potential congestion along an Internet path. The pattern of congestion signals (loss or ECN-marking) arising from AQM methods also needs to not adversely interact with the dynamics of the transport protocols that they control. AQM proposals should provide background material showing theoretical analysis of the AQM control law and the input parameter space within which the control law operates, or they should use another way to discuss the stability of the control law. For parameters that are auto-tuned, the material should include stability analysis of the auto-tuning mechanism(s) as well. Such analysis helps to understand an AQM control law better and the network conditions/deployments under which the AQM is stable.9. Various Traffic Profiles
This section provides guidelines to assess the performance of an AQM proposal for various traffic profiles such as traffic with different applications or bidirectional traffic.
9.1. Traffic Mix
This scenario can be used to evaluate how an AQM scheme reacts to a traffic mix consisting of different applications such as: o Bulk TCP transfer o Web traffic o VoIP o Constant Bit Rate (CBR) UDP traffic o Adaptive video streaming (either unidirectional or bidirectional) Various traffic mixes can be considered. These guidelines recommend examining at least the following example: 1 bidirectional VoIP; 6 web page downloads (such as those detailed in Section 7.2); 1 CBR; 1 Adaptive Video; 5 bulk TCP. Any other combinations could be considered and should be carefully documented. For each scenario, the graph described in Section 2.7 could be generated for each class of traffic. Metrics such as end-to-end latency, jitter, and flow completion time may be reported.9.2. Bidirectional Traffic
Control packets such as DNS requests/responses, TCP SYNs/ACKs are small, but their loss can severely impact the application performance. The scenario proposed in this section will help in assessing whether the introduction of an AQM scheme increases the loss probability of these important packets. For this scenario, traffic must be generated in both downlink and uplink, as defined in Section 3.1. The amount of asymmetry between the uplink and the downlink depends on the context. These guidelines recommend considering a mild congestion level and the traffic presented in Section 8.2.2 in both directions. In this case, the metrics reported must be the same as in Section 8.2 for each direction. The traffic mix presented in Section 9.1 may also be generated in both directions.
10. Example of a Multi-AQM Scenario
10.1. Motivation
Transports operating under the control of AQM experience the effect of multiple control loops that react over different timescales. It is therefore important that proposed AQM schemes are seen to be stable when they are deployed at multiple points of potential congestion along an Internet path. The pattern of congestion signals (loss or ECN-marking) arising from AQM methods also need to not adversely interact with the dynamics of the transport protocols that they control.10.2. Details on the Evaluation Scenario
+---------+ +-----------+ |senders A|---+ +---|receivers A| +---------+ | | +-----------+ +-----+---+ +---------+ +--+-----+ |Router L |--|Router M |--|Router R| |AQM A | |AQM M | |No AQM | +---------+ +--+------+ +--+-----+ +---------+ | | +-----------+ |senders B|-------------+ +---|receivers B| +---------+ +-----------+ Figure 3: Topology for the Multi-AQM Scenario Figure 3 describes topology options for evaluating multi-AQM scenarios. The AQM schemes are applied in sequence and impact the induced latency reduction, the induced goodput maximization, and the trade-off between these two. Note that AQM schemes A and B introduced in Routers L and M could be (I) same scheme with identical parameter values, (ii) same scheme with different parameter values, or (iii) two different schemes. To best understand the interactions and implications, the mild congestion scenario as described in Section 8.2.2 is recommended such that the number of flows is equally shared among senders A and B. Other relevant combinations of congestion levels could also be considered. We recommend measuring the metrics presented in Section 8.2.
11. Implementation Cost
11.1. Motivation
Successful deployment of AQM is directly related to its cost of implementation. Network devices may need hardware or software implementations of the AQM mechanism. Depending on a device's capabilities and limitations, the device may or may not be able to implement some or all parts of their AQM logic. AQM proposals should provide pseudocode for the complete AQM scheme, highlighting generic implementation-specific aspects of the scheme such as "drop-tail" vs. "drop-head", inputs (e.g., current queuing delay, and queue length), computations involved, need for timers, etc. This helps to identify costs associated with implementing the AQM scheme on a particular hardware or software device. This also facilitates discussions around which kind of devices can easily support the AQM and which cannot.11.2. Recommended Discussion
AQM proposals should highlight parts of their AQM logic that are device dependent and discuss if and how AQM behavior could be impacted by the device. For example, a queuing-delay-based AQM scheme requires current queuing delay as input from the device. If the device already maintains this value, then it can be trivial to implement the AQM logic on the device. If the device provides indirect means to estimate the queuing delay (for example, timestamps and dequeuing rate), then the AQM behavior is sensitive to the precision of the queuing delay estimations are for that device. Highlighting the sensitivity of an AQM scheme to queuing delay estimations helps implementers to identify appropriate means of implementing the mechanism on a device.12. Operator Control and Auto-Tuning
12.1. Motivation
One of the biggest hurdles of RED deployment was/is its parameter sensitivity to operating conditions -- how difficult it is to tune RED parameters for a deployment to achieve acceptable benefit from using RED. Fluctuating congestion levels and network conditions add to the complexity. Incorrect parameter values lead to poor performance. Any AQM scheme is likely to have parameters whose values affect the control law and behavior of an AQM. Exposing all these parameters as control parameters to a network operator (or user) can easily result
in an unsafe AQM deployment. Unexpected AQM behavior ensues when parameter values are set improperly. A minimal number of control parameters minimizes the number of ways a user can break a system where an AQM scheme is deployed at. Fewer control parameters make the AQM scheme more user-friendly and easier to deploy and debug. "AQM algorithms SHOULD NOT require tuning of initial or configuration parameters in common use cases." such as stated in Section 4 of the AQM recommendation document [RFC7567]. A scheme ought to expose only those parameters that control the macroscopic AQM behavior such as queue delay threshold, queue length threshold, etc. Additionally, the safety of an AQM scheme is directly related to its stability under varying operating conditions such as varying traffic profiles and fluctuating network conditions, as described in Section 8. Operating conditions vary often and hence the AQM needs to remain stable under these conditions without the need for additional external tuning. If AQM parameters require tuning under these conditions, then the AQM must self-adapt necessary parameter values by employing auto-tuning techniques.12.2. Recommended Discussion
In order to understand an AQM's deployment considerations and performance under a specific environment, AQM proposals should describe the parameters that control the macroscopic AQM behavior, and identify any parameters that require tuning to operational conditions. It could be interesting to also discuss that, even if an AQM scheme may not adequately auto-tune its parameters, the resulting performance may not be optimal, but close to something reasonable. If there are any fixed parameters within the AQM, their setting should be discussed and justified to help understand whether a fixed parameter value is applicable for a particular environment. If an AQM scheme is evaluated with parameter(s) that were externally tuned for optimization or other purposes, these values must be disclosed.13. Summary
Figure 4 lists the scenarios for an extended characterization of an AQM scheme. This table comes along with a set of requirements to present more clearly the weight and importance of each scenario. The requirements listed here are informational and their relevance may depend on the deployment scenario.
+------------------------------------------------------------------+ |Scenario |Sec. |Informational requirement | +------------------------------------------------------------------+ +------------------------------------------------------------------+ |Interaction with ECN | 4.5 |must be discussed if supported | +------------------------------------------------------------------+ |Interaction with Scheduling| 4.6 |should be discussed | +------------------------------------------------------------------+ |Transport Protocols | 5 | | | TCP-friendly sender | 5.1 |scenario must be considered | | Aggressive sender | 5.2 |scenario must be considered | | Unresponsive sender | 5.3 |scenario must be considered | | LBE sender | 5.4 |scenario may be considered | +------------------------------------------------------------------+ |Round-Trip Time Fairness | 6.2 |scenario must be considered | +------------------------------------------------------------------+ |Burst Absorption | 7.2 |scenario must be considered | +------------------------------------------------------------------+ |Stability | 8 | | | Varying congestion levels | 8.2.5|scenario must be considered | | Varying available capacity| 8.2.6|scenario must be considered | | Parameters and stability | 8.3 |this should be discussed | +------------------------------------------------------------------+ |Various Traffic Profiles | 9 | | | Traffic mix | 9.1 |scenario is recommended | | Bidirectional traffic | 9.2 |scenario may be considered | +------------------------------------------------------------------+ |Multi-AQM | 10.2 |scenario may be considered | +------------------------------------------------------------------+ Figure 4: Summary of the Scenarios and their Requirements14. Security Considerations
Some security considerations for AQM are identified in [RFC7567]. This document, by itself, presents no new privacy or security issues.15. References
15.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate Requirement Levels", RFC 2119, 1997. [RFC2544] Bradner, S. and J. McQuaid, "Benchmarking Methodology for Network Interconnect Devices", RFC 2544, DOI 10.17487/RFC2544, March 1999, <http://www.rfc-editor.org/info/rfc2544>.
[RFC2647] Newman, D., "Benchmarking Terminology for Firewall Performance", RFC 2647, DOI 10.17487/RFC2647, August 1999, <http://www.rfc-editor.org/info/rfc2647>. [RFC5481] Morton, A. and B. Claise, "Packet Delay Variation Applicability Statement", RFC 5481, DOI 10.17487/RFC5481, March 2009, <http://www.rfc-editor.org/info/rfc5481>. [RFC7567] Baker, F., Ed. and G. Fairhurst, Ed., "IETF Recommendations Regarding Active Queue Management", BCP 197, RFC 7567, DOI 10.17487/RFC7567, July 2015, <http://www.rfc-editor.org/info/rfc7567>. [RFC7679] Almes, G., Kalidindi, S., Zekauskas, M., and A. Morton, Ed., "A One-Way Delay Metric for IP Performance Metrics (IPPM)", STD 81, RFC 7679, DOI 10.17487/RFC7679, January 2016, <http://www.rfc-editor.org/info/rfc7679>. [RFC7680] Almes, G., Kalidindi, S., Zekauskas, M., and A. Morton, Ed., "A One-Way Loss Metric for IP Performance Metrics (IPPM)", STD 82, RFC 7680, DOI 10.17487/RFC7680, January 2016, <http://www.rfc-editor.org/info/rfc7680>.15.2. Informative References
[ANEL2014] Anelli, P., Diana, R., and E. Lochin, "FavorQueue: a Parameterless Active Queue Management to Improve TCP Traffic Performance", Computer Networks Vol. 60, DOI 10.1016/j.bjp.2013.11.008, 2014. [AQMPIE] Pan, R., Natarajan, P., Baker, F., and G. White, "PIE: A Lightweight Control Scheme To Address the Bufferbloat Problem", Work in Progress, draft-ietf-aqm-pie-08, June 2016. [BB2011] Cerf, V., Jacobson, V., Weaver, N., and J. Gettys, "BufferBloat: what's wrong with the internet?", ACM Queue Vol. 55, DOI 10.1145/2076450.2076464, 2012. [BCP41] Floyd, S., "Congestion Control Principles", BCP 41, RFC 2914, September 2000. Briscoe, B. and J. Manner, "Byte and Packet Congestion Notification", BCP 41, RFC 7141, February 2014. <http://www.rfc-editor.org/info/bcp41>
[CODEL] Nichols, K., Jacobson, V., McGregor, A., and J. Iyengar, "Controlled Delay Active Queue Management", Work in Progress, draft-ietf-aqm-codel-04, June 2016. [CUBIC] Rhee, I., Xu, L., Ha, S., Zimmermann, A., Eggert, L., and R. Scheffenegger, "CUBIC for Fast Long-Distance Networks", Work in Progress, draft-ietf-tcpm-cubic-01, January 2016. [FENG2002] Feng, W., Shin, K., Kandlur, D., and D. Saha, "The BLUE active queue management algorithms", IEEE Transactions on Networking Vol.10 Issue 4, DOI 10.1109/TNET.2002.801399, 2002, <http://ieeexplore.ieee.org/xpl/ articleDetails.jsp?arnumber=1026008>. [FLOY1993] Floyd, S. and V. Jacobson, "Random Early Detection (RED) Gateways for Congestion Avoidance", IEEE Transactions on Networking Vol. 1 Issue 4, DOI 10.1109/90.251892, 1993, <http://ieeexplore.ieee.org/xpl/ articleDetails.jsp?arnumber=251892>. [GONG2014] Gong, Y., Rossi, D., Testa, C., Valenti, S., and D. Taht, "Fighting the bufferbloat: on the coexistence of AQM and low priority congestion control", Computer Networks, Elsevier, 2014, pp.115-128 Vol. 60, DOI 10.1109/INFCOMW.2013.6562885, 2014. [HASS2008] Hassayoun, S. and D. Ros, "Loss Synchronization and Router Buffer Sizing with High-Speed Versions of TCP", IEEE INFOCOM Workshops, DOI 10.1109/INFOCOM.2008.4544632, 2008, <http://ieeexplore.ieee.org/xpl/ articleDetails.jsp?arnumber=4544632>. [HOEI2015] Hoeiland-Joergensen, T., McKenney, P., dave.taht@gmail.com, d., Gettys, J., and E. Dumazet, "The FlowQueue-CoDel Packet Scheduler and Active Queue Management Algorithm", Work in Progress, draft-ietf-aqm- fq-codel-06, March 2016. [HOLLO2001] Hollot, C., Misra, V., Towsley, V., and W. Gong, "On Designing Improved Controller for AQM Routers Supporting TCP Flows", IEEE INFOCOM, DOI 10.1109/INFCOM.2001.916670, 2001, <http://ieeexplore.ieee.org/xpl/ articleDetails.jsp?arnumber=916670>.
[JAY2006] Jay, P., Fu, Q., and G. Armitage, "A preliminary analysis of loss synchronisation between concurrent TCP flows", Australian Telecommunication Networks and Application Conference (ATNAC), 2006. [MORR2000] Morris, R., "Scalable TCP congestion control", IEEE INFOCOM, DOI 10.1109/INFCOM.2000.832487, 2000, <http://ieeexplore.ieee.org/xpl/ articleDetails.jsp?arnumber=832487>. [RFC793] Postel, J., "Transmission Control Protocol", STD 7, RFC 793, DOI 10.17487/RFC0793, September 1981, <http://www.rfc-editor.org/info/rfc793>. [RFC2309] Braden, B., Clark, D., Crowcroft, J., Davie, B., Deering, S., Estrin, D., Floyd, S., Jacobson, V., Minshall, G., Partridge, C., Peterson, L., Ramakrishnan, K., Shenker, S., Wroclawski, J., and L. Zhang, "Recommendations on Queue Management and Congestion Avoidance in the Internet", RFC 2309, DOI 10.17487/RFC2309, April 1998, <http://www.rfc-editor.org/info/rfc2309>. [RFC2488] Allman, M., Glover, D., and L. Sanchez, "Enhancing TCP Over Satellite Channels using Standard Mechanisms", BCP 28, RFC 2488, DOI 10.17487/RFC2488, January 1999, <http://www.rfc-editor.org/info/rfc2488>. [RFC3168] Ramakrishnan, K., Floyd, S., and D. Black, "The Addition of Explicit Congestion Notification (ECN) to IP", RFC 3168, DOI 10.17487/RFC3168, September 2001, <http://www.rfc-editor.org/info/rfc3168>. [RFC3611] Friedman, T., Ed., Caceres, R., Ed., and A. Clark, Ed., "RTP Control Protocol Extended Reports (RTCP XR)", RFC 3611, DOI 10.17487/RFC3611, November 2003, <http://www.rfc-editor.org/info/rfc3611>. [RFC5348] Floyd, S., Handley, M., Padhye, J., and J. Widmer, "TCP Friendly Rate Control (TFRC): Protocol Specification", RFC 5348, DOI 10.17487/RFC5348, September 2008, <http://www.rfc-editor.org/info/rfc5348>. [RFC5681] Allman, M., Paxson, V., and E. Blanton, "TCP Congestion Control", RFC 5681, DOI 10.17487/RFC5681, September 2009, <http://www.rfc-editor.org/info/rfc5681>.
[RFC6297] Welzl, M. and D. Ros, "A Survey of Lower-than-Best-Effort Transport Protocols", RFC 6297, DOI 10.17487/RFC6297, June 2011, <http://www.rfc-editor.org/info/rfc6297>. [RFC6817] Shalunov, S., Hazel, G., Iyengar, J., and M. Kuehlewind, "Low Extra Delay Background Transport (LEDBAT)", RFC 6817, DOI 10.17487/RFC6817, December 2012, <http://www.rfc-editor.org/info/rfc6817>. [RFC7141] Briscoe, B. and J. Manner, "Byte and Packet Congestion Notification", BCP 41, RFC 7141, DOI 10.17487/RFC7141, February 2014, <http://www.rfc-editor.org/info/rfc7141>. [TCPEVAL] Hayes, D., Ros, D., Andrew, L., and S. Floyd, "Common TCP Evaluation Suite", Work in Progress, draft-irtf-iccrg- tcpeval-01, July 2014. [TRAN2014] Trang, S., Kuhn, N., Lochin, E., Baudoin, C., Dubois, E., and P. Gelard, "On The Existence Of Optimal LEDBAT Parameters", IEEE ICC 2014 - Communication QoS, Reliability and Modeling Symposium, DOI 10.1109/ICC.2014.6883487, 2014, <http://ieeexplore.ieee.org/xpl/ articleDetails.jsp?arnumber=6883487>. [WELZ2015] Welzl, M. and G. Fairhurst, "The Benefits to Applications of using Explicit Congestion Notification (ECN)", Work in Progress, draft-welzl-ecn-benefits-02, March 2015. [WINS2014] Winstein, K., "Transport Architectures for an Evolving Internet", PhD thesis, Massachusetts Institute of Technology, June 2014.Acknowledgements
This work has been partially supported by the European Community under its Seventh Framework Programme through the Reducing Internet Transport Latency (RITE) project (ICT-317700). Many thanks to S. Akhtar, A.B. Bagayoko, F. Baker, R. Bless, D. Collier-Brown, G. Fairhurst, J. Gettys, P. Goltsman, T. Hoiland- Jorgensen, K. Kilkki, C. Kulatunga, W. Lautenschlager, A.C. Morton, R. Pan, G. Skinner, D. Taht, and M. Welzl for detailed and wise feedback on this document.
Authors' Addresses
Nicolas Kuhn (editor) CNES, Telecom Bretagne 18 avenue Edouard Belin Toulouse 31400 France Phone: +33 5 61 27 32 13 Email: nicolas.kuhn@cnes.fr Preethi Natarajan (editor) Cisco Systems 510 McCarthy Blvd Milpitas, California United States of America Email: prenatar@cisco.com Naeem Khademi (editor) University of Oslo Department of Informatics, PO Box 1080 Blindern N-0316 Oslo Norway Phone: +47 2285 24 93 Email: naeemk@ifi.uio.no David Ros Simula Research Laboratory AS P.O. Box 134 Lysaker, 1325 Norway Phone: +33 299 25 21 21 Email: dros@simula.no