| AQM | K. Nichols |
| Internet-Draft | Pollere, Inc. |
| Intended status: Experimental | V. Jacobson |
| Expires: March 15, 2018 | A. McGregor, ed. |
| J. Iyengar, ed. | |
| September 11, 2017 |
Controlled Delay Active Queue Management
draft-ietf-aqm-codel-08
This document describes a general framework called CoDel (Controlled Delay) that controls bufferbloat-generated excess delay in modern networking environments. CoDel consists of an estimator, a setpoint, and a control loop. It requires no configuration in normal Internet deployments.
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The "persistently full buffer" problem has been discussed in the IETF community since the early 80’s [RFC896]. The IRTF’s End-to-End Working Group called for the deployment of active queue management (AQM) to solve the problem in 1998 [RFC2309]. Despite this awareness, the problem has only gotten worse as Moore's Law growth in memory density fueled an exponential increase in buffer pool size. Efforts to deploy AQM have been frustrated by difficult configuration and negative impact on network utilization. This “bufferbloat” problem [TSV2011] [BB2011] has become increasingly important throughout the Internet but particularly at the consumer edge. Queue management has become more critical due to increased consumer use of the Internet, mixing large video transactions with time-critical VoIP and gaming.
An effective AQM remediates bufferbloat at a bottleneck while “doing no harm” at hops where buffers are not bloated. The development and deployment of AQM however is frequently subject to misconceptions about the cause of packet queues in network buffers. Network buffers exist to absorb the packet bursts that occur naturally in statistically multiplexed networks. Buffers helpfully absorb the queues created by such reasonable packet network behavior as short-term mismatches in traffic arrival and departure rates that arise from upstream resource contention, transport conversation startup transients and/or changes in the number of conversations sharing a link. Unfortunately, other less useful network behaviors can cause queues to fill and their effects are not nearly as benign. Discussion of these issues and the reason why the solution is not simply smaller buffers can be found in [RFC2309], [VANQ2006], [REDL1998], and [CODEL2012]. To understand queue management, it is critical to understand the difference between the necessary, useful “good” queue, and the counterproductive “bad” queue.
Several approaches to AQM have been developed over the past two decades but none has been widely deployed due to performance problems. When designed with the wrong conceptual model for queues, AQMs have limited operational range, require a lot of configuration tweaking, and frequently impair rather than improve performance. Learning from this past history, the CoDel approach is designed to meet the following goals:
CoDel has five major differences from prior AQMs: use of local queue minimum to track congestion (“bad queue”), use of an efficient single state variable representation of that tracked statistic, use of packet sojourn time as the observed datum, rather than packets, bytes, or rates, use of mathematically determined setpoint derived from maximizing network power [KLEIN81], and a modern state space controller.
CoDel configures itself based on a round-trip time metric which can be set to 100ms for the normal, terrestrial Internet. With no changes to parameters, CoDel is expected to work across a wide range of conditions, with varying links and the full range of terrestrial round trip times.
CoDel is easily adapted to multiple queue systems as shown by [FQ-CODEL-ID]. Implementers and users SHOULD use the fq_codel multiple-queue approach as it deals with many problems beyond the reach of an AQM on a single queue.
CoDel was first published in [CODEL2012] and has been implemented in the Linux kernel.
Note that while this document refers to dropping packets when indicated by CoDel, it is reasonable to ECN-mark packets instead as well.
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in [RFC2119].
In this document, these words will appear with that interpretation only when in ALL CAPS. Lower case uses of these words are not to be interpreted as carrying [RFC2119] significance.
At the heart of queue management is the notion of “good queue” and “bad queue” and the search for ways to get rid of the bad queue (which only adds delay) while preserving the good queue (which provides for good utilization). This section explains queueing, both good and bad, and covers the CoDel building blocks that can be used to manage packet buffers to keep their queues in the “good” range.
Packet queues form in buffers facing bottleneck links, i.e., where the line rate goes from high to low or where many links converge. The well-known bandwidth-delay product (sometimes called “pipe size”) is the bottleneck’s bandwidth multiplied by the sender-receiver-sender round-trip delay, and is the amount of data that has to be in transit between two hosts in order to run the bottleneck link at 100% utilization. To explore how queues can form, consider a long-lived TCP connection with a 25 packet window sending through a connection with a bandwidth-delay product of 20 packets. After an initial burst of packets the connection will settle into a five packet (+/-1) standing queue; this standing queue size is determined by the mismatch between the window size and the pipe size, and is unrelated to the connection’s sending rate. The connection has 25 packets in flight at all times, but only 20 packets arrive at the destination over a round trip time. If the TCP connection has a 30 packet window, the queue will be ten packets with no change in sending rate. Similarly, if the window is 20 packets, there will be no queue but the sending rate is the same. Nothing can be inferred about the sending rate from the queue size, and any queue other than transient bursts only creates delays in the network. The sender needs to reduce the number of packets in flight rather than sending rate.
In the above example, the five packet standing queue can be seen to contribute nothing but delay to the connection, and thus is clearly “bad queue”. If, in our example, there is a single bottleneck link and it is much slower than the link that feeds it (say, a high-speed ethernet link into a limited DSL uplink) a 20 packet buffer at the bottleneck might be necessary to temporarily hold the 20 packets in flight to keep the bottleneck link's utilization high. The burst of packets should drain completely (to 0 or 1 packets) within a round trip time and this transient queue is “good queue” because it allows the connection to keep the 20 packets in flight and for the bottleneck link to be fully utilized. In terms of the delay experienced, the “good queue” goes away in about a round trip time, while “bad queue” hangs around for longer, causing delays.
Effective queue management detects “bad queue” while ignoring “good queue” and takes action to get rid of the bad queue when it is detected. The goal is a queue controller that accomplishes this objective. To control a queue, we need three basic components
The estimator both observes the queue and detects when good queue turns to bad queue and vice versa. CoDel has two parts to its estimator: what is observed as an indicator of queue and how the observations are used to detect good/bad queue.
Queue length has been widely used as an observed indicator of congestion and is frequently conflated with sending rate. Use of queue length as a metric is sensitive to how and when the length is observed. A high speed arrival link to a buffer serviced at a much lower rate can rapidly build up a queue that might disperse completely or down to a single packet before a round trip time has elapsed. If the queue length is monitored at packet arrival (as in original RED) or departure time, every packet will see a queue with one possible exception. If the queue length itself is time sampled (as recommended in [REDL1998], a truer picture of the queue’s occupancy can be gained at the expense of considerable implementation complexity.
The use of queue length is further complicated in networks that are subject to both short and long term changes in available link rate (as in WiFi). Link rate drops can result in a spike in queue length that should be ignored unless it persists. It is not the queue length that should be controlled but the amount of excess delay packets experience due to a persistent or standing queue, which means that the packet sojourn time in the buffer is exactly what we want to track. Tracking the packet sojourn times in the buffer observes the actual delay experienced by each packet. Sojourn time allows queue management to be independent of link rate, gives superior performance to use of buffer size, and is directly related to user-visible performance. It works regardless of line rate changes or link sharing by multiple queues (which the individual queues may experience as changing rates).
Consider a link shared by two queues with different priorities. Packets that arrive at the high priority queue are sent as soon as the link is available while packets in the other queue have to wait until the high priority queue is empty (i.e., a strict priority scheduler). The number of packets in the high priority queue might be large but the queue is emptied quickly and the amount of time each packet spends enqueued (the sojourn time) is not large. The other queue might have a smaller number of packets, but packet sojourn times will include the waiting time for the high priority packets to be sent. This makes the sojourn time a good sample of the congestion that each separate queue is experiencing. This example also shows how the metric of sojourn time is independent of the number of queues or the service discipline used, and is instead indicative of congestion seen by the individual queues.
How can observed sojourn time be used to separate good queue from bad queue? Although averages, especially of queue length, have previously been widely used as an indicator of bad queue, their efficacy is questionable. Consider the burst that disperses every round trip time. The average queue will be one-half the burst size, though this might vary depending on when the average is computed and the timing of arrivals. The average queue sojourn time would be one-half the time it takes to clear the burst. The average then would indicate a persistent queue where there is none. Instead of averages we recommend tracking the minimum sojourn time, then, if there is one packet that has a zero sojourn time then there is no persistent queue. The value of the minimum in detecting persistent queue is apparent when looking at graphs of queue delay.
A persistent queue can be detected by tracking the (local) minimum queue delay packets experience. To ensure that this minimum value does not become stale, it has to have been experienced recently, i.e. during an appropriate past time interval. This interval is the maximum amount of time a minimum value is considered to be in effect, and is related to the amount of time it takes for the largest expected burst to drain. Conservatively, this interval SHOULD be at least a round trip time to avoid falsely detecting a persistent queue and not a lot more than a round trip time to avoid delay in detecting the persistent queue. This suggests that the appropriate interval value is the maximum round-trip time of all the connections sharing the buffer.
(The following key insight makes computation of the local minimum efficient: It is sufficient to keep a single state variable of how long the minimum has been above or below the target value rather than retaining all the local values to compute the minimum, leading to both storage and computational savings. We use this insight in the pseudo-code for CoDel later in the document.)
These two parts, use of sojourn time as observed values and the local minimum as the statistic to monitor queue congestion are key to CoDel’s estimator building block. The local minimum sojourn time provides an accurate and robust measure of standing queue and has an efficient implementation. In addition, use of the minimum sojourn time has important advantages in implementation. The minimum packet sojourn can only be decreased when a packet is dequeued which means that all the work of CoDel can take place when packets are dequeued for transmission and that no locks are needed in the implementation. The minimum is the only statistic with this property.
A more detailed explanation with many pictures can be found in http://www.ietf.org/proceedings/84/slides/slides-84-tsvarea-4.pdf.
Now that we have a robust way of detecting standing queue, we need a target setpoint that tells us when to act. If the controller is set to take action as soon as the estimator has a non-zero value, the average drop rate will be maximized, which minimizes TCP goodput [MACTCP1997]. Also, this policy results in no backlog over time (no persistent queue), which negates much of the value of having a buffer, since it maximizes the bottleneck link bandwidth lost due to normal stochastic variation in packet interarrival time. We want a target that maximizes utilization while minimizing delay. Early in the history of packet networking, Kleinrock developed the analytic machinery to do this using a quantity he called ‘power’, which is the ratio of a normalized throughput to a normalized delay [KLEIN81].
It is straightforward to derive an analytic expression for the average goodput of a TCP conversation at a given round-trip time r and target f (where f is expressed as a fraction of r). Reno TCP, for example, yields:
goodput = r (3 + 6f - f^2) / (4 (1+f))
Since the peak queue delay is simply the product of f and r, power is solely a function of f since the r’s in the numerator and denominator cancel:
power is proportional to (1 + 2f - 1/3 f^2) / (1 + f)^2
As Kleinrock observed, the best operating point, in terms of bandwidth / delay tradeoff, is the peak power point, since points off the peak represent a higher cost (in delay) per unit of bandwidth. The power vs. f curve for any Additive Increase Multiplicative Decrease (AIMD) TCP is monotone decreasing. But the curve is very flat for f < 0.1 followed by a increasing curvature with a knee around f = 0.2, then a steep, almost linear fall off [TSV84]. Since the previous equation showed that goodput is monotone increasing with f, the best operating point is near the right edge of the flat top since that represents the highest goodput achievable for a negligible increase in delay. However, since the r in the model is a conservative upper bound, a target of 0.1r runs the risk of pushing shorter RTT connections over the knee and giving them higher delay for no significant goodput increase. Generally, a more conservative target of 0.05r offers a good utilization vs. delay tradeoff while giving enough headroom to work well with a large variation in real RTT.
As the above analysis shows, a very small standing queue gives close to 100% utilization of the bottleneck link. While this result was for Reno TCP, the derivation uses only properties that must hold for any ‘TCP friendly’ transport. We have verified by both analysis and simulation that this result holds for Reno, Cubic, and Westwood [TSV84]. This results in a particularly simple form for the target: the ideal range for the permitted standing queue, or the target setpoint, is between 5% and 10% of the TCP connection's RTT.
We used simulation to explore the impact when TCPs are mixed with other traffic and with connections of different RTTs. Accordingly, we experimented extensively with values in the 5-10% of RTT range and, overall, used target values between 1 and 20 milliseconds for RTTs from 30 to 500ms and link bandwidths of 64Kbps to 100Mbps to experimentally explore a value for the target that gives consistently high utilization while controlling delay across a range of bandwidths, RTTs, and traffic loads. Our results were notably consistent with the mathematics above.
A congested (but not overloaded) CoDel link with traffic composed solely or primarily of long-lived TCP flows will have a median delay through the link will tend to the target. For bursty traffic loads and for overloaded conditions (where it is difficult or impossible for all the arriving flows to be accommodated) the median queues will be longer than the target.
The non-starvation drop inhibit feature dominates where the link rate becomes very small. By inhibiting drops when there is less than an (outbound link) MTU worth of bytes in the buffer, CoDel adapts to very low bandwidth links, as shown in [CODEL2012].
Section 3.1 describes a simple, reliable way to measure bad (persistent) queue. Section 3.2 shows that TCP congestion control dynamics gives rise to a target setpoint for this measure that’s a provably good balance between enhancing throughput and minimizing delay, and that this target is a constant fraction of the same 'largest average RTT' interval used to distinguish persistent from transient queue. The only remaining building block needed for a basic AQM is a 'control loop' algorithm to effectively drive the queueing system from any 'persistent queue above the target' state to a state where the persistent queue is below the target.
Control theory provides a wealth of approaches to the design of control loops. Most of classical control theory deals with the control of linear, time-invariant, single-input-single-output (SISO) systems. Control loops for these systems generally come from a (well understood) class known as Proportional-Integral-Derivative (PID) controllers. Unfortunately, a queue is not a linear system and an AQM operates at the point of maximum non-linearity (where the output link bandwidth saturates so increased demand creates delay rather than higher utilization). Output queues are also not time-invariant since traffic is generally a mix of connections which start and stop at arbitrary times and which can have radically different behaviors ranging from “open loop” UDP audio/video to “closed-loop” congestion-avoiding TCP. Finally, the constantly changing mix of connections (which can’t be converted to a single ‘lumped parameter’ model because of their transfer function differences) makes the system multi-input-multi-output (MIMO), not SISO.
Since queueing systems match none of the prerequisites for a classical controller, a modern state-space controller is a better approach with states ‘no persistent queue’ and ‘has persistent queue’. Since Internet traffic mixtures change rapidly and unpredictably, a noise and error tolerant adaptation algorithm like Stochastic Gradient is a good choice. Since there’s essentially no information in the amount of persistent queue [TSV84], the adaptation should be driven by how long it has persisted.
Consider the two extremes of traffic behavior, a single open-loop UDP video stream and a single, long-lived TCP bulk data transfer. If the average bandwidth of the UDP video stream is greater that the bottleneck link rate, the link’s queue will grow and the controller will eventually enter ‘has persistent queue’ state and start dropping packets. Since the video stream is open loop, its arrival rate is unaffected by drops so the queue will persist until the average drop rate is greater than the output bandwidth deficit (= average arrival rate - average departure rate) so the job of the adaptation algorithm is to discover this rate. For this example, the adaptation could consist of simply estimating the arrival and departure rates then dropping at a rate slightly greater than their difference. But this class of algorithm won’t work at all for the bulk data TCP stream. TCP runs in closed-loop flow balance [TSV84] so its arrival rate is almost always exactly equal to the departure rate - the queue isn’t the result of a rate imbalance but rather a mismatch between the TCP sender’s window and the source-destination-source round-trip path capacity (i.e., the connection’s bandwidth-delay product). The sender’s TCP congestion avoidance algorithm will slowly increase the send window (one packet per round-trip-time) [RFC2581] which will eventually cause the bottleneck to enter ‘has persistent queue’ state. But, since the average input rate is the same as the average output rate, the rate deficit estimation that gave the correct drop rate for the video stream would compute a drop rate of zero for the TCP stream. However, if the output link drops one packet as it enters ‘has persistent queue’ state, when the sender discovers this (via TCP’s normal packet loss repair mechanisms) it will reduce its window by a factor of two [RFC2581] so, one round-trip-time after the drop, the persistent queue will go away.
If there were N TCP conversations sharing the bottleneck, the controller would have to drop O(N) packets, one from each conversation, to make all the conversations reduce their window to get rid of the persistent queue. If the traffic mix consists of short (<= bandwidth-delay product) conversations, the aggregate behavior becomes more like the open-loop video example since each conversation is likely to have already sent all its packets by the time it learns about a drop so each drop has negligible effect on subsequent traffic.
The controller does not know the number, duration, or kind of conversations creating its queue, so it has to learn the appropriate response. Since single drops can have a large effect if the degree of multiplexing (the number of active conversations) is small, dropping at too high a rate is likely to have a catastrophic effect on throughput. Dropping at a low rate (< 1 packet per round-trip-time) then increasing the drop rate slowly until the persistent queue goes below the target is unlikely to overdrop and is guaranteed to eventually dissipate the persistent queue. This stochastic gradient learning procedure is the core of CoDel’s control loop (the gradient exists because a drop always reduces the (instantaneous) queue so an increasing drop rate always moves the system “down” toward no persistent queue, regardless of traffic mix).
The "next drop time" is decreased in inverse proportion to the square root of the number of drops since the drop state was entered, using the well-known nonlinear relationship of drop rate to throughput to get a linear change in throughput [REDL1998], [MACTCP1997].
Since the best rate to start dropping is at slightly more than one packet per RTT, the controller’s initial drop rate can be directly derived from the estimator’s interval. When the minimum sojourn time first crosses the target and CoDel drops a packet, the earliest the controller could see the effect of the drop is the round trip time (interval) + the local queue wait time (the target). If the next drop happens any earlier than this time (interval + target), CoDel will overdrop. In practice, the local queue waiting time tends to vary, so making the initial drop spacing (i.e., the time to the second drop) be exactly the minimum possible also leads to overdropping. Analysis of simulation and real-world measured data shows that the 75th percentile magnitude of this variation is less than the target, and so the initial drop spacing SHOULD be set to the estimator’s interval (i.e., initial drop spacing = interval) to ensure that the controller has accounted for acceptable congestion delays.
Use of the minimum statistic lets the controller be placed in the dequeue routine with the estimator. This means that the control signal (the drop) can be sent at the first sign of bad queue (as indicated by the sojourn time) and that the controller can stop acting as soon as the sojourn time falls below the target. Dropping at dequeue has both implementation and control advantages.
CoDel was initially designed as a bufferbloat solution for the consumer network edge. The CoDel building blocks are able to adapt to different or time-varying link rates, to be easily used with multiple queues, to have excellent utilization with low delay, and to have a simple and efficient implementation.
The CoDel algorithm described in the rest of this document uses two key variables: TARGET, which is the controller's target setpoint described in Section 3.2 and INTERVAL, which is the estimator's interval described in Section 3.3.
The only setting CoDel requires is the INTERVAL value, and as 100ms satisfies that definition for normal Internet usage, CoDel can be parameter-free for consumer use. To ensure that link utilization is not adversely affected, CoDel’s estimator sets TARGET to one that optimizes power. CoDel’s controller does not drop packets when the drop would leave the queue empty or with fewer than a maximum transmission unit (MTU) worth of bytes in the buffer. Section 3.2 shows that an ideal TARGET is 5-10% of the connection round trip time (RTT). In the open terrestrial-based Internet, especially at the consumer edge, we expect most unbloated RTTs to have a ceiling of 100ms [CHARB2007]. Using this RTT gives a minimum TARGET of 5ms and INTERVAL of 100ms. In practice, uncongested links will see sojourn times below TARGET more often than once per RTT, so the estimator is not overly sensitive to the value of INTERVAL.
When the estimator finds a persistent delay above TARGET, the controller enters the drop state where a packet is dropped and the next drop time is set. As discussed in section 3.3, the initial next drop spacing is intended to be long enough to give the endpoints time to react to the single drop so SHOULD be set to a value equal to INTERVAL. If the estimator’s output falls below TARGET, the controller cancels the next drop and exits the drop state. (The controller is more sensitive than the estimator to an overly short INTERVAL value, since an unnecessary drop would occur and lower link utilization.) If next drop time is reached while the controller is still in drop state, the packet being dequeued is dropped and the next drop time is recalculated.
Additional logic prevents re-entering the drop state too soon after exiting it and resumes the drop state at a recent control level, if one exists. This logic is described more precisely in the pseudo-code below. Additional work is required to determine the frequency and importance of re-entering the drop state.
Note that CoDel AQM only enters its drop state when the local minimum sojourn delay has exceeded TARGET for a time period long enough for normal bursts to dissipate, ensuring that a burst of packets that fits in the pipe will not be dropped.
CoDel’s goals are to control delay with little or no impact on link utilization and to be deployed on a wide range of link bandwidths, including variable-rate links, without reconfiguration. To keep from making drops when it would starve the output link, CoDel makes another check before dropping to see if at least an MTU worth of bytes remains in the buffer. If not, the packet SHOULD NOT be dropped and, therefore, CoDel exits the drop state. The MTU size can be set adaptively to the largest packet seen so far or can be read from the interface driver.
The INTERVAL value is chosen to give endpoints time to react to a drop without being so long that response times suffer. CoDel’s estimator, TARGET, and control loop all use INTERVAL. Understanding their derivation shows that CoDel is the most sensitive to the value of INTERVAL for single long-lived TCPs with a decreased sensitivity for traffic mixes. This is fortunate as RTTs vary across connections and are not known a priori. The best policy seems to be to use an INTERVAL value slightly larger than the RTT seen by most of the connections using a link, a value that can be determined as the largest RTT seen if the value is not an outlier (use of a 95-99th percentile value should work). In practice, this value is not known or measured (though see section 6.2 for an application where INTERVAL is measured). An INTERVAL setting of 100ms works well across a range of RTTs from 10ms to 1 second (excellent performance is achieved in the range from 10 ms to 300ms). For devices intended for the normal terrestrial Internet, INTERVAL SHOULD have a value of 100ms. This will only cause overdropping where a long-lived TCP has an RTT longer than 100ms and there is little or no mixing with other connections through the link.
TARGET is the maximum acceptable persistent queue delay above which CoDel is dropping or preparing to drop and below which CoDel will not drop. TARGET SHOULD be set to 5ms for normal Internet traffic.
The calculations of section 3.2 show that the best TARGET value is 5-10% of the RTT, with the low end of 5% preferred. Extensive simulations exploring the impact of different TARGET values when used with mixed traffic flows with different RTTs and different bandwidths show that below a TARGET of 5ms, utilization suffers for some conditions and traffic loads, and above 5ms showed very little or no improvement in utilization.
Sojourn times must remain above the TARGET for INTERVAL amount of time in order to enter the drop state. Any packet with a sojourn time less than TARGET will reset the time that the queue was last below TARGET. Since Internet traffic has very dynamic characteristics, the actual sojourn delay experienced by packets varies greatly and is often less than TARGET unless the overload is excessive. When a link is not overloaded, it is not a bottleneck and packet sojourn times will be small or nonexistent. In the usual case, there are only one or two places along a path where packets will encounter a bottleneck (usually at the edge), so the total amount of queueing delay experienced by a packet should be less than 10ms even under extremely congested conditions. This net delay is substantially lower than common current queueing delays on the Internet that grow to orders of seconds [NETAL2010, CHARB2007].
A note on the roles of TARGET and the minimum-tracking INTERVAL. TARGET SHOULD NOT be increased in response to lower layers that have a bursty nature, where packets are transmitted for short periods interspersed with idle periods where the link is waiting for permission to send. CoDel’s estimator will “see” the effective transmission rate over an INTERVAL amount of time, and increasing TARGET only leads to longer queue delays. On the other hand, where a significant additional delay is added to the intrinsic RTT of most or all packets due to the waiting time for a transmission, it is necessary to increase INTERVAL by that extra delay. TARGET SHOULD NOT be adjusted for such short-term bursts, but INTERVAL MAY need to be adjusted if the path's intrinsic RTT changes.
CoDel is easily adapted to multiple queue systems. With other approaches there is always a question of how to account for the fact that each queue receives less than the full link rate over time and usually sees a varying rate over time. This is what CoDel excels at: using a packet’s sojourn time in the buffer completely circumvents this problem. In a multiple-queue setting, a separate CoDel algorithm runs on each queue, but each CoDel instance uses the packet sojourn time the same way a single-queue CoDel does. Just as a single-queue CoDel adapts to changing link bandwidths [CODEL2012], so does a multiple-queue CoDel system. As an optimization to avoid queueing more than necessary, when testing for queue occupancy before dropping, the total occupancy of all queues sharing the same output link SHOULD be used. This property of CoDel has been exploited in fq_codel [FQ-CODEL-ID], which hashes on the packet header fields to determine a specific bin, or sub-queue, for the packet, and runs CoDel on each bin or sub-queue thus creating a well-mixed output flow and obviating issues of reverse path flows (including “ack compression”).
CoDel is set for use in devices in the open Internet. An INTERVAL setting of 100ms is used, TARGET is set to 5% of INTERVAL, and the initial drop spacing is also set to the INTERVAL. These settings have been chosen so that a device, such as a small WiFi router, can be sold without the need for any values to be made adjustable, yielding a parameterless implementation. In addition, CoDel is useful in environments with significantly different characteristics from the normal Internet, for example, in switches used as a cluster interconnect within a data center. Since cluster traffic is entirely internal to the data center, round trip latencies are low (typically <100us) but bandwidths are high (1-40Gbps) so it's relatively easy for the aggregation phase of a distributed computation (e.g., the Reduce part of a Map/Reduce) to persistently fill then overflow the modest per-port buffering available in most high speed switches. A CoDel configured for this environment (TARGET and INTERVAL in the microsecond rather than millisecond range) can minimize drops or ECN marks while keeping throughput high and latency low.
Devices destined for these environments MAY use a different value for INTERVAL, where suitable. If appropriate analysis indicates, the TARGET MAY be set to some other value in the 5-10% of INTERVAL and the initial drop spacing MAY be set to a value of 1.0 to 1.2 times INTERVAL. But these settings will cause problems such as overdropping and low throughput if used on the open Internet, so devices that allow CoDel to be configured SHOULD default to Internet-appropriate values given in this document.
What follows is the CoDel algorithm in C++-like pseudo-code. Since CoDel adds relatively little new code to a basic tail-drop fifo-queue, we have attempted to highlight just these additions by presenting CoDel as a sub-class of a basic fifo-queue base class. The reference code is included to aid implementers who wish to apply CoDel to queue management as described here or to adapt its principles to other applications.
Implementors are strongly encouraged to also look at the Linux kernel version of CoDel - a well-written, well tested, real-world, C-based implementation. As of this writing, it is available at https://github.com/torvalds/linux/blob/master/net/sched/sch_codel.c.
time_t is an integer time value in units convenient for the system. The code presented here uses 0 as a flag value to indicate "no time set."
packet_t* is a pointer to a packet descriptor. We assume it has a tstamp field capable of holding a time_t and that field is available for use by CoDel (it will be set by the enqueue routine and used by the dequeue routine).
queue_t is a base class for queue objects (the parent class for codel_queue_t objects). We assume it has enqueue() and dequeue() methods that can be implemented in child classes. We assume it has a bytes() method that returns the current queue size in bytes. This can be an approximate value. The method is invoked in the dequeue() method but shouldn't require a lock with the enqueue() method.
flag_t is a Boolean.
time_t first_above_time_ = 0; // Time to declare sojourn time above
// TARGET
time_t drop_next_ = 0; // Time to drop next packet
uint32_t count_ = 0; // Packets dropped in drop state
uint32_t lastcount_ = 0; // Count from previous iteration
flag_t dropping_ = false; // Set to true if in drop state
time_t TARGET = MS2TIME(5); // 5ms TARGET queue delay
time_t INTERVAL = MS2TIME(100); // 100ms sliding-minimum window
u_int maxpacket = 512; // Maximum packet size in bytes
// (SHOULD use interface MTU)
All the work of CoDel is done in the dequeue routine. The only CoDel addition to enqueue is putting the current time in the packet’s tstamp field so that the dequeue routine can compute the packet's sojourn time.
void codel_queue_t::enqueue(packet_t* pkt)
{
pkt->timestamp() = clock();
queue_t::enqueue(pkt);
}
This is the heart of CoDel. There are two branches based on whether the controller is in drop state: (i) if the controller is in drop state (that is, the minimum packet sojourn time is greater than TARGET) then the controller checks if it is time to leave drop state or schedules the next drop(s); or (ii) if the controller is not in drop state, it determines if it should enter drop state and do the initial drop.
packet_t* CoDelQueue::dequeue()
{
time_t now = clock();
dodequeue_result r = dodequeue(now);
uint32_t delta;
if (dropping_) {
if (! r.ok_to_drop) {
// sojourn time below TARGET - leave drop state
dropping_ = false;
}
// Time for the next drop. Drop current packet and dequeue
// next. If the dequeue doesn't take us out of dropping
// state, schedule the next drop. A large backlog might
// result in drop rates so high that the next drop should
// happen now, hence the 'while' loop.
while (now >= drop_next_ && dropping_) {
drop(r.p);
++count_;
r = dodequeue(now);
if (! r.ok_to_drop) {
// leave drop state
dropping_ = false;
} else {
// schedule the next drop.
drop_next_ = control_law(drop_next_, count_);
}
}
// If we get here we're not in drop state. The 'ok_to_drop'
// return from dodequeue means that the sojourn time has been
// above 'TARGET' for 'INTERVAL' so enter drop state.
} else if (r.ok_to_drop) {
drop(r.p);
r = dodequeue(now);
dropping_ = true;
// If min went above TARGET close to when it last went
// below, assume that the drop rate that controlled the
// queue on the last cycle is a good starting point to
// control it now. ('drop_next' will be at most 'INTERVAL'
// later than the time of the last drop so 'now - drop_next'
// is a good approximation of the time from the last drop
// until now.) Implementations vary slightly here; this is
// the Linux version, which is more widely deployed and
// tested.
delta = count_ - lastcount_;
count_ = 1;
if ((delta > 1) && (now - drop_next_ < 16*INTERVAL))
count_ = delta;
drop_next_ = control_law(now, count_);
lastcount_ = count_;
}
return (r.p);
}
Since the degree of multiplexing and nature of the traffic sources is unknown, CoDel acts as a closed-loop servo system that gradually increases the frequency of dropping until the queue is controlled (sojourn time goes below TARGET). This is the control law that governs the servo. It has this form because of the sqrt(p) dependence of TCP throughput on drop probability. Note that for embedded systems or kernel implementation, the inverse sqrt can be computed efficiently using only integer multiplication.
time_t codel_queue_t::control_law(time_t t, uint32_t count)
{
return t + INTERVAL / sqrt(count);
}
Next is a helper routine the does the actual packet dequeue and tracks whether the sojourn time is above or below TARGET and, if above, if it has remained above continuously for at least INTERVAL amount of time. It returns two values: a Boolean indicating if it is OK to drop (sojourn time above TARGET for at least INTERVAL), and the packet dequeued.
typedef struct {
packet_t* p;
flag_t ok_to_drop;
} dodequeue_result;
dodequeue_result codel_queue_t::dodequeue(time_t now)
{
dodequeue_result r = { queue_t::dequeue(), false };
if (r.p == NULL) {
// queue is empty - we can't be above TARGET
first_above_time_ = 0;
return r;
}
// To span a large range of bandwidths, CoDel runs two
// different AQMs in parallel. One is sojourn-time-based
// and takes effect when the time to send an MTU-sized
// packet is less than TARGET. The 1st term of the "if"
// below does this. The other is backlog-based and takes
// effect when the time to send an MTU-sized packet is >=
// TARGET. The goal here is to keep the output link
// utilization high by never allowing the queue to get
// smaller than the amount that arrives in a typical
// interarrival time (MTU-sized packets arriving spaced
// by the amount of time it takes to send such a packet on
// the bottleneck). The 2nd term of the "if" does this.
time_t sojourn_time = now - r.p->tstamp;
if (sojourn_time_ < TARGET || bytes() <= maxpacket_) {
// went below - stay below for at least INTERVAL
first_above_time_ = 0;
} else {
if (first_above_time_ == 0) {
// just went above from below. if still above at
// first_above_time, will say it's ok to drop.
first_above_time_ = now + INTERVAL;
} else if (now >= first_above_time_) {
r.ok_to_drop = true;
}
}
return r;
}
time_t is an integer time value in units convenient for the system. Resolution to at least a millisecond is required and better resolution is useful up to the minimum possible packet time on the output link; 64- or 32-bit widths are acceptable but with 32 bits the resolution should be no finer than 2^{-16} to leave enough dynamic range to represent a wide range of queue waiting times. Narrower widths also have implementation issues due to overflow (wrapping) and underflow (limit cycles because of truncation to zero) that are not addressed in this pseudocode.
Since CoDel requires relatively little per-queue state and no direct communication or state sharing between the enqueue and dequeue routines, it is relatively simple to add CoDel to almost any packet processing pipeline, including ASIC- or NPU-based forwarding engines. One issue to consider is dodequeue()'s use of a 'bytes()' function to determine the current queue size in bytes. This value does not need to be exact. If the enqueue part of the pipeline keeps a running count of the total number of bytes it has put into the queue and the dequeue routine keeps a running count of the total bytes it has removed from the queue, 'bytes()' is simply the difference between these two counters (32-bit counters should be adequate.) Enqueue has to update its counter once per packet queued but it does not matter when (before, during or after the packet has been added to the queue). The worst that can happen is a slight, transient, underestimate of the queue size which might cause a drop to be briefly deferred.
We encourage experimentation with the recommended values of TARGET and INTERVAL for Internet settings. CoDel provides general, efficient, parameterless building blocks for queue management that can be applied to single or multiple queues in a variety of data networking scenarios. CoDel’s settings may be modified for other special-purpose networking applications.
This document describes an active queue management algorithm for implementation in networked devices. There are no known security exposures associated with CoDel at this time.
This document does not require actions by IANA.
The authors thank Jim Gettys for the constructive nagging that made us get the work “out there” before we thought it was ready. We thank Dave Taht, Eric Dumazet, and the open source community for showing the value of getting it “out there” and for making it real. We thank Nandita Dukkipati for contributions to section 6 and for comments which helped to substantially improve this draft. We thank the AQM working group and the Transport Area shepherd, Wes Eddy, for patiently prodding this draft all the way to a standard.
| [RFC2119] | Bradner, S., "Key Words for use in RFCs to Indicate Requirement Levels", March 1997. |
Nandita Dukkipati and her group at Google realized that the CoDel building blocks could be applied to bufferbloat problems in datacenter servers, not just to Internet routers. The Linux CoDel queueing discipline (qdisc) was adapted in three ways to tackle this bufferbloat problem.
Non-data packets were not dropped as these are typically small and sometimes critical control packets. Being located on the server, there is no concern with misbehaving users as there would be on the public Internet.
In several data center workload benchmarks, which are typically bursty, CoDel reduced the queueing latencies at the qdisc, and thereby improved the mean and 99th-percentile latencies from several tens of milliseconds to less than one millisecond. The minimum tracking part of the CoDel framework proved useful in disambiguating “good” queue versus “bad” queue, particularly helpful in controlling qdisc buffers that are inherently bursty because of TCP Segmentation Offload (TSO).