QUIC is a relatively new data transport protocol developed by Google, and currently in line to become the default transport for the upcoming HTTP standard. Because of this, it behooves every network engineer to understand a little about this protocol, how it operates, and what impact it will have on the network. We did record a History of Networking episode on QUIC, if you want some background.

In a recent Communications of the ACM article, a group of researchers (Kakhi et al.) used a modified implementation of QUIC to measure its performance under different network conditions, directly comparing it to TCPs performance under the same conditions. Since the current implementations of QUIC use the same congestion control as TCP—Cubic—the only differences in performance should be code tuning in estimating the round-trip timer (RTT) for congestion control, QUIC’s ability to form a session in a single RTT, and QUIC’s ability to carry multiple streams in a single connection. The researchers asked two questions in this paper: how does QUIC interact with TCP flows on the same network, and does UIC perform better than TCP in all situations, or only some?

To answer the first question, the authors tried running QUIC and TCP over the same network in different configurations, including single QUIC and TCP sessions, a single QUIC session with multiple TCP sessions, etc. In each case, they discovered that QUIC consumed about 50% of the bandwidth; if there were multiple TCP sessions, they would be starved for bandwidth when running in parallel with the QUIC session. For network folk, this means an application implemented using QUIC could well cause performance issues for other applications on the network—something to be aware of. This might mean it is best, if possible, to push QUIC-based applications into a separate virtual or physical topology with strict bandwidth controls if it causes other applications to perform poorly.

Does QUIC’s ability to consume more bandwidth mean applications developed on top of it will perform better? According to the research in this paper, the answer is how many balloons fit in a bag? In other words, it all depends. QUIC does perform better when its multi-stream capability comes into play and the network is stable—for instance, when transferring variably sized objects (files) across a network with stable jitter and delay. In situations with high jitter or delay, however, TCP consistently outperforms QUIC.

TCP outperforming QUIC is a bit of a surprise in any situation; how is this possible? The researchers used information from their additional instrumentation to discover QUIC does not tolerate out-of-order packet delivery very well because of its fast packet retransmission implementation. Presumably, it should be possible to modify these parameters somewhat to make QUIC perform better.

This would still leave the second problem the researchers found with QUIC’s performance—a large difference between its performance on desktop and mobile platforms. The difference between these two comes down to where QUIC is implemented. Desktop devices (and/or servers) often have smart NICs which implement TCP in the ASIC to speed packet processing up. QUIC, because it runs in user space, only runs on the main processor (it seems hard to see how a user space application could run on a NIC—it would probably require a specialized card of some type, but I’ll have to think about this more). The result is that QUIC’s performance depends heavily on the speed of the processor. Since mobile devices have much slower processors, QUIC performs much more slowly on mobile devices.

QUIC is an interesting new transport protocol—one everyone involved in designing or operating networks is eventually going to encounter. This paper gives good insight into the “soul” of this new protocol.

When you think of new Ethernet standards, you probably think about faster and optical. There is, however, an entire world of buildings out there with older copper cabling, particularly in the industrial realm, that could see dramatic improvements in productivity if their control and monitoring systems could be moved to IP. In these cases, what is needed is an Ethernet standard that runs over a single copper pair, and yet offers enough speed to support industrial use cases. Peter Jones joins Jeremy Filliben and Russ White to discuss single pair Ethernet.


The IETF works on many things beyond IP and routing—the Media Operations (MOPS) working group is gathering input on media-related operational issues and practices, including “proposed technologies related to the deployment, engineering, and operation of media streaming and manipulation protocols and procedures in the global Internet (inter-domain) and within-domain networking.” Leslie Daigle and Eric Vyncke, the co-chairs of the MOPS working group, join Alvaro Retana and Russ White to discuss the work they are doing.


There was a time when Software Defined Networking was going to take over the entire networking world—just like ATM, FDDI, and … so many others before. Whatever happened to SDN, anyway? What is its enduring legacy in the world of network engineering? Terry Slattery, Tom Ammon, and Russ White gather at the hedge to have a conversation about whatever happened to SDN?


MultiPath TCP (MPTCP) is an effort towards enabling the simultaneous use of several IP-addresses/interfaces by a modification of TCP that presents a regular TCP interface to applications, while in fact spreading data across several subflows. Benefits of this include better resource utilization, better throughput and smoother reaction to failures.

You can learn more about M-TCP here and here.


According to the recent SONAR report, 52% of respondents reported they are using Software Defined Networking (SDN) tools to automate their networks, while 57% reported they are using network management tools. The report notes “52% may be slightly exaggerated, depending on how one defines SDN…” Which leads naturally to the question—what the difference between SDN and DevOps is, and how does AI figure into both or either of these.  SDN, DevOps, and AI describe separate and overlapping movements in the design, deployment, and management of networks. While they are easy to confuse, they have three different origins and meanings.

Software Defined Networking grew out of research efforts to build and deploy experimental control planes, either distributed or centralized. SDN, however, quickly became associated with replacing some or all the functions of a distributed control plane with a centralized controller, particularly in order to centralize policy related to the control plane such as traffic engineering. SDN solutions always work through a programmatic interface designed to primarily supply forwarding information to network devices.

Development Operations, or DevOps, is a movement away from human-centered interfaces towards machine-centered interfaces for the deployment, operation, and troubleshooting of networks. DevOps is centered on the deployment, configuration, and management of the entire device, rather than providing the information required to forward traffic. DevOps can either use a programmatic interface, such as YANG, or “screen scraping,” to configure and manage network devices.

Finally, Artificial Intelligence, or AI, in the context of computer networks, is focused on the use of data gathered from the network to improve operations, from decreasing the time required to troubleshoot a problem to making the network adapt more quickly to shifting application and business requirements. AI, applied to networks, is narrow in scope, so it is Artificial Narrow Intelligence, or ANI. Real implementations of AI in the networking field are often applications of Machine Learning, or ML; while these two terms are often used interchangeably, they are not quite the same thing.

The following illustration will be useful in understanding the relationship between these three concepts.


In the figure, the SDN and DevOps controllers interact with two different aspects of the network devices forwarding traffic; both SDN and DevOps can be deployed in the same network to solve different problems. For instance, DevOps might be used to configure network devices to reach the SDN controller so they can receive the information they need to forward packets. Or the DevOps system might be used to configure a distributed control plane, such as IS-IS, on all the network devices, and also to configure a centralized controller which can override the local decisions of the distribute routing protocol for traffic engineering.

There are some situations where the difference between SDN and DevOps solutions is not obvious. The most common example is DevOps could be used to configure routing information on each network device, performing the same function as an SDN controller. In this case, what is the difference?

First, an SDN solution is intended specifically to replace the distributed control plane, rather than to configure the entire device. Second, the configurations pushed to a device through DevOps is normally persistent; if a device reboots, the configuration pushed through DevOps will be loaded and enabled, impacting the operation of the device. In contrast, any information pushed to a device through an SDN controller would normally be ephemeral; when the device is rebooted, information pushed by the SDN controller will be lost.

Finally, AI and self-healing are shown on the right side of this diagram as a way to turn telemetry into actionable input for either the DevOps or the SDN system. The ability of ML networks to find and recognize patterns in streams of data means it is perfectly suited to find new patterns of network behavior and alert an operator, or to match current conditions to the past, anticipating future failures or finding an otherwise unnoticed problem.

While SDN, DevOps, and AI overlap, then, they serve different purposes in the realm of network engineering and operations. There are many areas of overlap, but they are also different enough to argue the three terms should be cleanly separated, with each adding a different kind of value to the overall system.