Chapter 7: Beyond TCP

As exploration of the design space for congestion control has continued, a number of new algorithms and protocols have emerged. These differ from what we’ve seen in earlier chapters mostly in that they target specific use cases, rather than the arbitrarily complex and heterogeneous network environments that TCP supports. The exception may be QUIC, which started with the goal of improving HTTP performance specifically, but has now developed into something of a general TCP alternative.

This chapter is not exhaustive, but we instead survey a few specific use cases. These include tuning TCP performance for datacenters; sending background traffic over an extended period of time using only excess capacity; optimizing HTTP-based web traffic without being backward-compatible with TCP; supporting real-time streaming in a way that is TCP-friendly; supporting multipath transport protocols; and accommodating mobile cellular networks with unique radio-induced behavior.

7.1 Datacenters (DCTCP, On-Ramp)

There have been several efforts to optimize TCP for cloud datacenters, where Data Center TCP was one of the first. There are several aspects of the datacenter environment that warrant an approach that differs from more traditional TCP. These include:

  • Round trip time for intra-DC traffic are small;

  • Buffers in datacenter switches are also typically small;

  • All the switches are under common administrative control, and thus can be required to meet certain standards;

  • A great deal of traffic has low latency requirements;

  • That traffic competes with high bandwidth flows.

It should be noted that DCTCP is not just a version of TCP, but rather, a system design that changes both the switch behavior and the end host response to congestion information received from switches.

The central insight in DCTCP is that using loss as the main signal of congestion in the datacenter environment is insufficient. By the time a queue has built up enough to overflow, low latency traffic is already failing to meet its deadlines, negatively impacting performance. Thus DCTCP uses a version of ECN to provide an early signal of congestion. But whereas the original design of ECN treated an ECN marking much like a dropped packet, and cut the congestion window in half, DCTCP takes a more finely-tuned approach. DCTCP tries to estimate the fraction of bytes that are encountering congestion rather than making the simple binary decision that congestion is present. It then scales the congestion window based on this estimate. The standard TCP algorithm still kicks in should a packet actually be lost. The approach is designed to keep queues short by reacting early to congestion while not over-reacting to the point that they run empty and sacrifice throughput.

The key challenge in this approach is to estimate the fraction of bytes encountering congestion. Each switch is simple. If a packet arrives and the switch sees the queue length (K) is above some threshold; e.g.,

\[\mathsf{K} > \mathsf{(RTT} \times \mathsf{C)\ /\ 7}\]

where C is the link rate in packets per second, then the switch sets the CE bit in the IP header. The complexity of RED is not required.

The receiver then maintains a boolean variable for every flow, which we’ll denote DCTCP.CE, and sets it initially to false. When sending an ACK, the receiver sets the ECE (Echo Congestion Experienced) flag in the TCP header if and only if DCTCP.CE is true. It also implements the following state machine in response to every received packet:

  • If the CE bit is set and DCTCP.CE=False, set DCTCP.CE to True and send an immediate ACK.

  • If the CE bit is not set and DCTCP.CE=True, set DCTCP.CE to False and send an immediate ACK.

  • Otherwise, ignore the CE bit.

The non-obvious consequence of the “otherwise” case is that the receiver continues to send delayed ACKs once every n packets, as long as a stream of packets with a constant CE value is received. Delayed ACKs have proven important to maintaining high performance.

At the end of each observation window (a period usually chosen to be approximately the RTT), the sender computes the fraction of bytes that encountered congestion during that window as the ratio of the bytes marked with CE to total bytes transmitted. DCTCP grows the congestion window in exactly the same way as the standard algorithm, but it reduces the window in proportion to how many bytes encountered congestion during the last observation window.

Specifically, a new variable called DCTCP.Alpha is initialized to 1 and updated at the end of the observation window as follows:

\[\mathsf{DCTCP.Alpha} = \mathsf{DCTCP.Alpha} \times \mathsf{(1 - g) + g} \times \mathsf{M}\]

M is the faction of bytes marked, and g is the estimation gain, a constant (set by the implementation) that determines how rapidly DCTCP.Alpha changes in response to marking of packets. When there is sustained congestion, DCTCP.Alpha approaches 1, and when there is sustained lack of congestion, DCTCP.Alpha decays to zero. This causes gentle reaction to newly arrived congestion and more severe reaction to sustained congestion, as the congestion window is calculated as follows:

\[\mathsf{CongestionWindow} = \mathsf{CongestionWindow} \times \mathsf{(1 - DCTCP.Alpha\ /\ 2)}\]

To summarize, CE marking to indicate incipient congestion happens early and often, but the reaction to such marking is more measured than in standard TCP, to avoid the over-reaction that would lead to queues running empty.

The paper that lays out all the arguments for DCTCP including a study of the datacenter traffic characteristics that motivated its design is a “test of time” award winner from SIGCOMM.

Further Reading

M. Alizadeh, et al. Data Center TCP (DCTCP). ACM SIGCOMM, August 2010.

There has been considerable research since DCTCP to optimize TCP for datacenters, with the general approach being to introduce ever-more sophisticated signals from the network that the sender can use to manage congestion. We conclude our discussion of this use case by elaborating on one of the most recent efforts, On-Ramp, because it focuses instead on the fundamental tension that all congestion control algorithms face: The trade-off between reaching equilibrium for long-lived flows versus dealing with transient bursts. On-Ramp adopts a modular design that directly addresses this tension, and does so without depending on additional feedback from the network.

The main insight is that when a congestion control algorithm in equilibrium encounters severe congestion and drastically cuts its window (or rate), it must decide whether or not to remember its previous equilibrium state. This is a difficult choice because it depends on the duration of congestion, which is hard to predict. If the congestion is transient, the algorithm should remember its previous state so it can rapidly restore the old equilibrium without under-utilizing the network once the burst ends. If the congestion is sustained, for example due to the arrival of one or more new flows, the algorithm should forget its previous state so that it can rapidly find a new equilibrium.

_images/Slide13.png

Figure 41. On-Ramp paces packet transmission to avoid in-network queues due to bursty traffic, complementing the traditional congestion control algorithm’s effort to maintain long-term stability and fairness.

The idea is to break the congestion control mechanism into two parts, with each focused on just one aspect of the equilibrium/transient trade-off. Specifically, On-Ramp is implemented as a “shim” that sits below a conventional TCP congestion control algorithm, as shown in Figure 41. The On-Ramp shim deals with bursts (which temporarily fill network queues) by trying to quickly reduce queuing delays whenever the measured One-Way Delay (OWD) grows too large. It does this by temporarily holding packets at the sender (rather than letting them occupy an in-network buffer) whenever OWD is greater than some threshold. The On-Ramp shim is then composed with an existing congestion control algorithm, which continues to work towards reaching equilibrium for long-term flows. On-Ramp has been shown to work with several existing congestion control algorithms, including DCTCP.

The key is that On-Ramp is designed so the two control decisions run independently, on their own timescale. But to work, the shim needs to accurately measure OWD, which in turn depends on synchronized clocks between the sender and receiver. Since datacenter delays can be less than a few tens of microseconds, the sender and receiver clocks must be synchronized to within a few microseconds. Such high-accuracy clock synchronization has traditionally required hardware-intensive protocols, but On-Ramp leverages a new approach that takes advantage of the network effect in a mesh of cooperating nodes to achieve nanosecond-level clock synchronization. It does so without special hardware, making On-Ramp easy to deploy.

7.2 Background Transport (LEDBAT)

In sharp contrast to low-latency datacenter environments, there are many applications that need to transfer a large amount of data over an extended period of time. File-sharing protocols such as BitTorrent and software-updates are two examples. LEDBAT (Low Extra Delay Background Transport) is targeted at these applications.

One of the common themes among various efforts to improve TCP’s congestion control algorithm has been the idea of co-existence with standard TCP. It is well-known that an algorithm could “outperform” TCP by simply being more aggressive in its response to congestion. Hence, there is an implicit assumption that new congestion control algorithms should be evaluated alongside standard TCP to ensure they are not just stealing bandwidth from less aggressive TCP implementations.

LEDBAT takes this idea in the opposite direction by creating a congestion control protocol that is purposely less aggressive than TCP. The idea is to take advantage of bandwidth that is available when links are uncongested, but to quickly back off and leave the bandwidth free for other, standard flows when they arrive. In addition, as the name suggests, LEDBAT tries not to create significant queuing delays, unlike the typical behavior of TCP when filling a bottleneck link.

Like TCP Vegas, LEDBAT aims to detect the onset of congestion before it is severe enough to cause loss. However, LEDBAT takes a different approach to making this determination, using one-way measurements of delay as the primary input to the process. This is a relatively novel approach that makes sense in an era where reasonably accurate but not perfectly synchronized clocks are assumed to be the norm.

To calculate one-way delay, the sender puts a timestamp in each transmitted packet, and the receiver compares this against local system time to measure the delay experienced by the packet. It then sends this calculated value back to the sender. Even though the clocks are not precisely synchronized, changes in this delay can be used to infer the buildup of queues. It is assumed that the clocks do not have large relative “skew”, i.e., their relative offset does not change too quickly, which is a reasonable assumption in practice.

The sender monitors the measured delay, and estimates the fixed component (which would be due to speed of light and other fixed delays) to be the lowest value seen over a certain (configurable) time interval. Estimates from the more distant past are eliminated to allow for the possibility of a new routing path changing the fixed delay. Any delay larger than this minimum is assumed to be due to queuing delay.

Having established a “base” delay, the sender subtracts this from the measured delay to obtain the queuing delay, and optionally uses a filtering algorithm to reduce short-term noise in the estimate. This estimated queuing delay is then compared to a target delay. When the delay is below target, the congestion window is allowed to grow, and when the delay is above target, the congestion window is reduced, with the rate of growth and decrease being proportional to the distance from the target. The growth rate is capped to be no faster than the growth of standard TCP’s window in its additive increase phase.

LEDBAT’s algorithm for setting CongestionWindow when an ACK is received can be summarized as follows:

\[\mathsf{CongestionWindow}\ = \mathsf{CongestionWindow + (GAIN × off\_target × bytes\_newly\_acked × MSS / CongestionWindow)}\]

where GAIN is a configuration parameter between 0 and 1, off_target is the gap between the measured queuing delay and the target, expressed as a fraction of the target, and bytes_newly_acked is the number of bytes acknowledged in the current ACK. Thus, the congestion window grows more quickly the further the measured delay is below the target, but never faster than one MSS per RTT. And it falls faster in proportion to how far the queue length is above the target. CongestionWindow is also reduced in response to losses, timeouts, and long idle periods, much like with TCP.

Hence, LEDBAT can do a good job of using available bandwidth that is free, but avoids creating long standing queues, as it aims to keep the delay around the target (which is a configurable number, suggested to be on the order of 100 ms). If other traffic starts to compete with LEDBAT traffic, LEDBAT will back off as it aims to prevent the queue getting longer.

LEDBAT is defined as an experimental protocol by the IETF, and allows a considerable degree of implementation flexibility such as the choice of filtering on delay estimates and a range of configuration parameters. Further details can be found in the RFC.

Further Reading

S. Shalunov, et al. Low Extra Delay Background Transport (LEDBAT). RFC 6817, December 2012.

7.3 HTTP Performance (QUIC)

HTTP has been around since the invention of the World Wide Web in the 1990s and from its inception it has run over TCP. HTTP/1.0, the original version, had quite a number of performance problems due to the way it used TCP, such as the fact that every request for an object required a new TCP connection to be set up and then closed after the reply was returned. HTTP/1.1 was proposed at an early stage to make better use of TCP. TCP continued to be the protocol used by HTTP for another twenty-plus years.

In fact, TCP continued to be problematic as a protocol to support the Web, especially because a reliable, ordered byte stream isn’t exactly the right model for Web traffic. In particular, since most web pages contain many objects, it makes sense to be able to request many objects in parallel, but TCP only provides a single byte stream. If one packet is lost, TCP waits for its retransmission and successful delivery before continuing, while HTTP would have been happy to receive other objects that were not affected by that single lost packet. Opening multiple TCP connections would appear to be a solution to this, but that has its own set of drawbacks including a lack of shared information about congestion across connections.

Other factors such as the rise of high-latency wireless networks, the availability of multiple networks for a single device (e.g., Wi-Fi and cellular), and the increasing use of encrypted, authenticated connections on the Web also contributed to the realization that the transport layer for HTTP would benefit from a new approach. The protocol that emerged to fill this need was QUIC.

QUIC originated at Google in 2012 and was subsequently developed as a proposed standard at the IETF. It has already seen a solid amount of deployment—it is in most Web browsers, many popular websites, and is even starting to be used for non-HTTP applications. Deployability was a key consideration for the designers of the protocol. There are a lot of moving parts to QUIC—its specification spans three RFCs of several hundred pages—but we focus here on its approach to congestion control, which embraces many of the ideas we have seen to date in this book.

Like TCP, QUIC builds congestion control into the transport, but it does so in a way that recognizes that there is no single perfect congestion control algorithm. Instead, there is an assumption that different senders may use different algorithms. The baseline algorithm in the QUIC specification is similar to TCP NewReno, but a sender can unilaterally choose a different algorithm to use, such as CUBIC. QUIC provides all the machinery to detect lost packets in support of various congestion control algorithms.

A number of design features of QUIC make the detection of loss and congestion more robust than in TCP. For example, whereas TCP uses the same sequence number for a packet whether it is being sent for the first time or retransmitted, QUIC sequence numbers (called packet numbers) are strictly increasing. A higher packet number signifies that the packet was sent later, and a lower packet number signifies that the packet was sent earlier. This means that it is always possible to distinguish between a packet that has been transmitted for the first time and one that has been retransmitted due to a loss or timeout.

Note also that whereas TCP sequence numbers refer to bytes in the transmitted byte stream, QUIC packet numbers refer to entire packets. The packet number space for QUIC is large enough to avoid wraparound issues (up to 2^62 - 1).

QUIC builds selective acknowledgments into the protocol, with support for more than the three ranges of packets that can be acknowledged in the TCP SACK option. This improves performance in high loss environments, enabling forward progress to be made as long as some packets are getting received successfully.

QUIC adopts a more robust approach to determining packet loss than the duplicate ACKs on which TCP Fast Recovery relies. The approach was developed independent of QUIC under the name RACK-TLP: Recent Acknowledgments and Tail Loss Probes. A key insight is that duplicate ACKs fail to trigger loss recovery when the sender doesn’t send enough data after the lost packet to trigger the duplicate ACKs, or when retransmitted packets are themselves lost. Conversely, packet reordering may also trigger fast recovery when in fact no packets have been lost. QUIC takes the ideas of RACK-TLP to address this by using a pair of mechanisms:

  • A packet is considered lost if a packet with a higher number has been acknowledged, and the packet was sent “long enough in the past” or K packets before the acknowledged packet (K is a parameter).

  • Probe packets are sent after waiting a “probe timeout interval” for an ACK to arrive, in an effort to trigger an ACK that will provide information about lost packets.

The first bullet ensures that modest amounts of packet reordering are not interpreted as loss events. K is recommended to be initially set to 3, but can be updated if there is evidence of greater misordering. And the definition of “long enough in the past” is a little more than the measured RTT.

The second bullet ensures that, even if duplicate ACKs are not generated by data packets, probe packets are sent to elicit further ACKs, thus exposing gaps in the received packet stream. The “probe timeout interval” is calculated to be just long enough to account for all the delays that an ACK might have encountered, using both the estimated RTT and an estimate of its variance.

QUIC is a most interesting development in the world of transport protocols. Many of the limitations of TCP have been known for decades, but QUIC represents one of the most successful efforts to date to stake out a different point in the design space. It has also built in decades worth of experience refining TCP congestion control into the baseline specification. Because QUIC was inspired by experience with HTTP and the Web—which arose long after TCP was well established in the Internet—it presents a fascinating case study in the unforeseen consequences of layered designs and in the evolution of the Internet. There is a lot more to it that we can cover here. The definitive reference for QUIC is RFC 9000, but congestion control is covered in the separate RFC 9002.

Further Reading

J. Iyengar and I. Swett, Eds. QUIC Loss Detection and Congestion Control. RFC 9002, May 2021.

7.4 TCP-Friendly Protocols (TFRC)

As noted at various points throughout this book, it is easy to make transport protocols that out-perform TCP, since TCP in all its forms backs off when it detects congestion. Any protocol which does not respond to congestion with a reduction in sending rate will eventually get a bigger share of the bottleneck link than any TCP or TCP-like traffic that it competes against. In the limit, this would likely lead back to the congestion collapse that was starting to become common when TCP congestion control was first developed. Hence, there is a strong interest in making sure that the vast majority of traffic on the Internet is in some sense “TCP-friendly”.

When we use the term “TCP-friendly” we are saying that we expect a similar congestion response to that of TCP. LEDBAT could be considered “more than TCP-friendly” in the sense that it backs off even more aggressively to congestion than TCP by reducing its window size at the first hint of delay. But there is a class of applications for which being TCP-friendly requires a bit more thought because they do not use a window-based congestion scheme. These are typically “real time” applications involving streaming multimedia.

Multimedia applications such as video streaming and telephony can adjust their sending rate by changing coding parameters, with a trade-off between bandwidth and quality. However, they cannot suddenly reduce sending rate by a large amount without a perceptible impact on the quality, and they generally need to choose among a finite set of quality levels. These considerations lead to rate-based approaches rather than window-based, as discussed in Section 3.1.

The approach to TCP-friendliness for these applications is to try to pick a sending rate similar to that which would be achieved by TCP under similar conditions, but to do so in a way that keeps the rate from fluctuating too wildly. Underpinning this idea is a body of research going back many years on modeling the throughput of TCP. A simplified version of the TCP throughput equation is given in RFC 5348 which defines the standard for TFRC. With a few variables set to recommended values, the equation for target transmit rate X in bits/sec is:

\[\mathsf{X} = \frac{s}{R\times\sqrt{2p/3} + 12\sqrt{3p/8}\times p \times (1 + 32 p^2)}\]

Where:

  • s is the segment size (excluding IP and transport headers);

  • R is the RTT in seconds;

  • p is the number of “loss events” as a fraction of packets transmitted.

While the derivation of this formula is interesting in its own right (see the second reference below), the key idea here is that we have a pretty good idea of how much bandwidth a TCP connection will be able to deliver if we know the RTT and the loss rate of the path. So TFRC tries to steer applications that cannot implement a window-based congestion control algorithm to arrive at the same throughput as TCP would under the same conditions.

The only issues remaining to be addressed are the measurement of p and R, and then deciding how the application should respond to changes in X. Like some of the other protocols we have seen, TFRC uses timestamps to measure RTT more accurately than TCP originally did. Packet sequence numbers are used to determine packet loss at the receiver, with consecutive losses grouped into a single loss event. From this information the loss event rate p can be calculated at the receiver who then reflects it back to the sender.

Exactly how the application responds to a change in rate will of course depend on the application. The basic idea would be that an application can choose among a set of coding rates, and it picks the highest quality that can be accommodated with the rate that TFRC dictates.

While the concept of TFRC is solid, it has had limited deployment for a number of reasons. One is that a simpler solution for some types of streaming traffic emerged in the form of DASH (Dynamic Adaptive Streaming over HTTP). DASH is only suitable for non-real-time media (e.g., watching movies) but that turns out to be a large percentage of the media traffic that runs across the Internet—in fact, it is a large percentage of all Internet traffic.

DASH lets TCP (or potentially QUIC) take care of congestion control; the application measures the throughput that TCP is delivering, then adjusts the quality of the video stream accordingly to avoid starvation at the receiver. This approach has proven to be suitable for video entertainment, but since it depends on a moderately large amount of buffering at the receiver to smooth out the fluctuations in TCP throughput, it is not really suitable for interactive audio or video. One of the key realizations that made DASH feasible was the idea that one could encode video at multiple quality levels with different bandwidth requirements, and store them all in advance on a streaming server. Then, as soon as the observed throughput of the network drops, the server can drop to a lower quality stream, and then ramp up to higher quality as conditions permit. The client can send information back to the server, such as how much buffered video it still has awaiting playback, to help the server choose a suitable quality and bandwidth stream. The cost of this approach is additional media storage on the server, but that cost has become rather unimportant in the modern streaming video era. Note that the “server” in this context is likely to be a node in a CDN (content distribution network). Hence, a video stream can take advantage of any improvement in the bandwidth available between a client and the CDN node serving it by shifting a higher quality level.

Another limitation of TFRC as defined is that it uses loss as its primary signal of congestion but does not respond to the delay that precedes loss. While this was the state of the art when work on TFRC was undertaken, the field of TCP congestion control has now moved on to take delay into account, as in the case of TCP Vegas and BBR (see Chapter 5). And this is particularly problematic when you consider that the class of multimedia applications that really need something other than DASH are precisely those applications for which delay is important. For this reason, work continues at the time of writing to define standards for TCP-friendly congestion control for real-time traffic. The IETF RMCAT (RTP Media Congestion Avoidance Techniques) working group is the home of this work. The specification of TFRC below therefore is not the final work, but gives useful background on how one might go about implementing a TCP-friendly protocol.

Further Reading

S. Floyd, M. Handley, J. Padhye, and J. Widmer. TCP Friendly Rate Control (TFRC): Protocol Specification. RFC 5348, September 2008.

Further Reading

J. Padhye, V. Firoiu, D. Towsley, and J. Kurose. Modeling TCP Throughput: A Simple Model and its Empirical Validation. ACM SIGCOMM, September 1998.

7.5 Multipath Transport

While the early hosts connected to the Internet had only a single network interface, it is common these days to have interfaces to at least two different networks on a device. The most common example is a mobile phone with both cellular and WiFi interfaces. Another example is datacenters, which often allocate multiple network interfaces to servers to improve fault tolerance. Many applications use only one of the available networks at a time, but the potential exists to improve performance by using multiple interfaces simultaneously. This idea of multipath communication has been around for decades and led to a body of work at the IETF to standardize extensions to TCP to support end-to-end connections that leverage multiple paths between pairs of hosts. This is known as Multipath TCP (MPTCP).

A pair of hosts sending traffic over two or more paths simultaneously has implications for congestion control. For example, if both paths share a common bottleneck link, then a naive implementation of one TCP connection per path would acquire twice as much share of the bottleneck bandwidth as a standard TCP connection. The designers of MPTCP set out to address this potential unfairness while also realizing the benefits of multiple paths. The proposed congestion control approach could equally be applied to other transports such as QUIC. The high level goals of congestion control for multipath transport are:

  1. Perform at least as well as a single path flow on its best available path.

  2. Do not take more resources from any path than a single path flow would take.

  3. Move us much traffic as possible off the most congested path(s), consistent with the two preceding goals.

It’s worth noting that the idea of fairness to other TCP flows has some subtleties, which we touched on in Section 3.2.

While the details of the multipath algorithm involve complex bookkeeping, the overall approach taken is straightforward. The congestion control algorithm roughly mimics that of TCP on a per-subflow basis, while trying to ensure that all three goals above are met. The core of the algorithm uses the following formula to increase the congestion window size of each individual subflow as ACKs are received on the subflow.

\[\mathsf{MIN} (\frac{\alpha \times \mathsf{BytesAcked} \times \mathsf{MSS_{i}}}{\mathsf{CongestionWindowTotal}}, \frac{\mathsf{BytesAcked} \times \mathsf{MSS_{i}}}{\mathsf{CongestionWindow_{i}}} )\]

\(\mathsf{CongestionWindowTotal}\) is the sum of congestion windows across all subflows, while \(\mathsf{CongestionWindow_{i}}\) is the congestion window of subflow i. The second argument to MIN mimics the increase that standard TCP would obtain, thus ensuring that the subflow is no more aggressive than TCP (goal 2). The first argument uses the variable \(\alpha\) to ensure that, in aggregate, the multipath flow obtains the same throughput as it would have done using its best available path (goal 1). The calculation of \(\alpha\) is described in detail in RFC 6356. Note that uncongested paths are able to grow their individual congestion windows more than congested paths as they do not suffer losses, and hence over time, more traffic moves onto the uncongested paths (goal 3).

While this is simple enough in retrospect, a lot of interesting analysis went into figuring out the right approach, as described in an NSDI paper by Wischik and colleagues.

Further Reading

D. Wischik, C. Raiciu, A. Greenhalgh and M. Handley. Design, Implementation and Evaluation of Congestion Control for Multipath TCP. NSDI, April 2011.

C. Raiciu, M. Handley and D. Wischik. Coupled Congestion Control for Multipath Transport Protocols. RFC 6356, October 2011.

7.6 Mobile Cellular Networks

We conclude with a use case that continues to attract attention from the research community: the interplay between congestion control and the mobile cellular network. Historically, the TCP/IP Internet and the mobile cellular network evolved independently, with the latter serving as the “last mile” for end-to-end TCP connections since the introduction of broadband service with 3G. With the rollout of 5G now ramping up, we can expect the mobile network will play an increasingly important role in providing Internet connectivity, putting increased focus on how it impacts congestion control.

While a mobile wireless connection could be viewed as no different than any other hop along an end-to-end path through the Internet, for historical reasons it has been treated as a special case, with the end-to-end path logically divided into the two segments depicted in Figure 42: the wired segment through the Internet and the wireless last-hop over the Radio Access Network (RAN). This “special case” perspective is warranted because (1) the wireless link is typically the bottleneck due to the scarcity of radio spectrum; (2) the bandwidth available in the RAN can be highly variable due to a combination of device mobility and radio interference; and (3) the number of devices being served by a given base station fluctuates as devices move from one cell to another.

_images/Slide12.png

Figure 42. End-to-end path that includes a last-hop wireless link, where the base station buffers packets awaiting transmission over the Radio Access Network (RAN).

Although the internals of the RAN are largely closed and proprietary, researchers have experimentally observed that there is significant buffering at the edge, presumably to absorb the expected contention for the radio link, and yet keep sufficient work “close by” for whenever capacity does open up. As noted by Haiqing Jiang and colleagues in their 2012 CellNet workshop paper, this large buffer is problematic for TCP congestion control because it causes the sender to overshoot the actual bandwidth available on the radio link, and in the process, introduces significant delay and jitter. This is another example of the bufferbloat problem identified in Section 6.3.

Further Reading

H. Jiang, Z. Liu, Y. Wang, K. Lee and I. Rhee. Understanding Bufferbloat in Cellular Networks ACM SIGCOMM Workshop on Cellular Networks, August 2012.

The Jiang paper suggests possible solutions, and generally observes that delay-based approaches like Vegas outperform loss-based approaches like Reno or CUBIC, but the problem has remained largely unresolved for nearly a decade. With the promise of open source software-based implementations of the RAN now on the horizon (see our companion 5G and SDN books for more details), it might soon be possible to take a cross-layer approach, whereby the RAN provides an interface that give higher layers of the protocol stack (e.g., the AQM mechanisms described in Chapter 6) visibility into what goes on inside the base station. Recent research by Xie, Yi, and Jamieson suggests such an approach might prove effective, although their implementation uses end-device feedback instead of getting the RAN directly involved. How ever it’s implemented, the idea is to have the receiver explicitly tell the sender how much bandwidth is available on the last hop, with the sender then having to judge whether the last-hop or some other point along the Internet segment is the actual bottleneck.

Further Reading

Y. Xie, F. Yi, and K. Jamieson. PBE-CC: Congestion Control via Endpoint-Centric, Physical-Layer Bandwidth Measurements. SIGCOMM 2020.

L. Peterson and O. Sunay. 5G Mobile Networks: A Systems Approach. January 2020.

L. Peterson, C. Cascone, B. O’Connor, T. Vachuska, and and B. Davie. Software-Defined Networks: A Systems Approach. November 2021.

The other aspect of cellular networks that makes them a novel challenge for TCP congestion control is that the bandwidth of a link is not constant, but instead varies as a function of the signal-to-noise ratio experienced by each receiver. As noted by the BBR authors, the (currently opaque) scheduler for this wireless link can use the number of queued packets for a given client as an input to its scheduling algorithm, and hence the “reward” for building up a queue can be an increase in bandwidth provided by the scheduler. BBR has attempted to address this in its design by ensuring that it is aggressive enough to queue at least some packets in the buffers of wireless links.

Past research inquiries aside, it’s interesting to ask if the wireless link will remain all that unique going forward. If you take a compartmentalized view of the world, and you’re a mobile network operator, then your goal has historically been to maximize utilization of the scarce radio spectrum under widely varying conditions. Keeping the offered workload as high as possible, with deep queues, is a proven way to do that. This certainly made sense when broadband connectivity was the new service and voice and text were the dominant use cases, but today 5G is all about delivering good TCP performance. The focus should be on end-to-end goodput and maximizing the throughput/latency ratio (i.e., the power curve discussed in Section 3.2). But is there an opportunity for improvement?

We believe the answer to this question is yes. In addition to providing more visibility into the RAN scheduler and queues mentioned earlier, three other factors have the potential to change the equation. First, 5G deployments will likely support network slicing, a mechanism that isolates different classes of traffic. This means each slice has its own queue that can be sized and scheduled in a traffic-specific way. Second, the proliferation of small cells will likely reduce the number of flows competing for bandwidth at a given base station. How this impacts the scheduler’s approach to maximizing spectrum utilization is yet to be seen. Third, it will become increasingly common for 5G-connected devices to be served from a nearby edge cloud rather than from the other side of the Internet. This means end-to-end TCP connections will have much shorter round-trip times, which will make the congestion control algorithm more responsive to changes in the available capacity in the RAN. There are no guarantees, of course, but all these factors should provide ample opportunities to tweak congestion control algorithms well into the future.