Real-time communication (RTC) applications require low packet delay and loss while maintaining a stable throughput. WebRTC, a widely used framework, relies on Google Congestion Control (GCC). Meanwhile, newer methods like L4S (Low Latency, Low Loss, Scalable Throughput) provide opportunities with early congestion signaling. In this work, we extend client-side WebRTC to support L4S by adding ECN-based rate control and ECN feedback handling. Our results show that L4S improves real-time performance while remaining compatible with current WebRTC infrastructure.

Video-conferencing applications face an unwavering surge in traffic, stressing their underlying infrastructure in unprecedented ways. This paper rethinks the key building block for conferencing infrastructures — selective forwarding units (SFUs). SFUs relay and adapt media streams between participants and, today, run in software on general-purpose servers. Our main insight, discerned from dissecting the operation of production SFU servers, is that SFUs largely mimic traditional packet-processing operations such as dropping and forwarding. Guided by this, we present Scallop, an SDN-inspired SFU that decouples video-conferencing applications into a hardware-based data plane for latency-sensitive and frequent media operations, and a software control plane for the (infrequent) remaining tasks, such as analyzing feedback signals and session management. Scallop is a general design that is suitable for a variety of hardware platforms, including programmable switches and SmartNICs. Our Tofino-based implementation fully supports WebRTC and delivers 7-422× improved scaling over a 32-core commodity server, while reaping performance improvements by cutting forwarding-induced latency by 26×. We also present an implementation of Scallop on the BlueField-3 SmartNIC.

It is notoriously difficult to verify that a network is behaving as intended, especially at scale. This paper presents Hydra, a system that uses ideas from runtime verification to check that every packet is correctly processed with respect to a specification in real time. We propose a domain-specific language for writing properties, called Indus, and we develop a compiler that turns properties thus specified into executable P4 code that runs alongside the forwarding code at line rate. To evaluate our approach, we used Indus to model a range of properties, showing that it is expressive enough to capture examples studied in prior work. We also deployed Hydra checkers for validating paths in source routing and for enforcing slice isolation in Aether, an open-source cellular platform. We confirmed a subtle bug in Aether's 5G mobile core that would have been hard to detect using static techniques. We also evaluated the overheads of Hydra on hardware, finding that it does not significantly increase latency and often does not require additional pipeline stages.

Enterprise networks face increasing threats against the privacy of their clients. Existing enterprise services like Network Address Translation (NAT) offer limited privacy protection, at the cost of requiring per-flow state. In this paper, we introduce RAVEN (Rapid Address Variation for Enterprise Networks), a network-based privacy solution that is complementary to application-layer defenses. RAVEN protects privacy by frequently changing the client's public IP address. With RAVEN, a client is not limited to using a single IP address at a given time, or even for a given connection. RAVEN goes further, breaking the association between packets that belong to the same connection by frequently changing the client's IP address within a single connection. RAVEN achieves this through a novel division of labor: the client uses a transport protocol, like QUIC, that supports seamless connection migration, and decides when to switch its IP address, while the enterprise network actually changes the client's IP address in a stateless manner at line rate and ensures end-to-end packet delivery. We implement RAVEN using QUIC and off-the-shelf programmable switches. We deploy RAVEN in a test IPv6 network and evaluate its defense against webpage fingerprinting attacks. Even with a strong adversary, the average precision of the best adaptive attacks drops from 0.96 to 0.84, with a 0.5% degradation in client throughput. When RAVEN changes IP addresses at unpredictable frequency, the precision of the best attacks falls to 0.78---the same effectiveness as WTF-PAD.

Video-conferencing applications impose high loads and stringent performance requirements on the network. To better understand and manage these applications, we need effective ways to measure performance in the wild. For example, these measurements would help network operators in capacity planning, troubleshooting, and setting QoS policies. Unfortunately, large-scale measurements of production networks cannot rely on end-host cooperation, and an in-depth analysis of packet traces requires knowledge of the header formats. Zoom is one of the most sophisticated and popular applications, but it uses a proprietary network protocol. In this paper, we demystify how Zoom works at the packet level, and design techniques for analyzing Zoom performance from packet traces. We conduct systematic controlled experiments to discover the relevant unencrypted fields in Zoom packets, as well as how to group streams into meetings and how to identify peer-to-peer meetings. We show how to use the header fields to compute metrics like media bit rates, frame sizes and rates, and latency and jitter, and demonstrate the value of these fine-grained metrics on a 12-hour trace of Zoom traffic on our campus network.

Operating System (OS) fingerprinting allows network administrators to identify which operating systems are running on the hosts communicating over their network. This information is useful for detecting OS-specific vulnerabilities and for administering OS-related security policies that block, rate-limit, or redirect traffic. Passive fingerprinting can identify hosts’ OS types without active probes that introduce additional network load. However, existing software-based passive fingerprinting tools cannot keep up with the traffic in high-speed networks. This paper presents P40f, a tool that runs on programmable switch hardware to perform OS fingerprinting and apply security policies at line rate. Unlike p0f, P40f can fingerprint devices’ OS types and react to it (e.g., drop, rate-limit) in real time directly in the switch, without requiring any control-plane messages. P40f is a P4 implementation of an existing software tool, p0f. We present our prototype implemented with the P4 language, which compiles and runs on the Intel Tofino switch. We present experiments against packet traces from a real campus network, and make our code publicly available.

Round-trip time (RTT) is a central metric that influences end-user QoE and can expose traffic-interception attacks. Many popular RTT monitoring techniques either send active probes (that do not capture application-level RTTs) or passively monitor only the TCP handshake (which can be inaccurate, especially for long-lived flows). High-speed programmable switches present a unique opportunity to monitor the RTTs continuously and react in real time to improve performance and security. In this paper, we present Dart, an inline, real-time, and continuous RTT measurement system that can enable automated detection of network events and adapt (e.g., routing, scheduling, marking, or dropping traffic) inside the network. However, designing Dart is fraught with challenges, due to the idiosyncrasies of the TCP protocol and the resource constraints in high-speed switches. Dart overcomes these challenges by strategically limiting the tracking of packets to only those that can generate useful RTT samples, and by identifying the synergy between per-flow state and per-packet state for efficient memory use. We present a P4 prototype of Dart for the Tofino switch, as well our experiments on a campus testbed and simulations using anonymized campus traces. Dart, running in real time and with limited data-plane memory, is able to collect 99% of the RTT samples of an offline, software baseline---a variant of the popular tcptrace tool that has access to unlimited memory.

Internet Service Providers (ISPs) have introduced "data caps", or quotas on the amount of data that a customer can download during a billing cycle. Under this model, Internet users who reach a data cap can be subject to degraded performance, extra fees, or even temporary interruption of Internet service. For this reason, users need better visibility into and control over their Internet usage to help them understand what uses up data and control how these quotas are reached. In this paper, we present the design and implementation of a tool, called uCap, to help home users manage Internet data. We conducted a field trial of uCap in 21 home networks in three countries and performed an in-depth qualitative study of ten of these homes. We present the results of the evaluation and implications for the design of future Internet data management tools.

We present the first empirical study of home network availability, infrastructure, and usage, using data collected from home networks around the world. In each home, we deploy a router with custom firmware to collect information about the availability of home broadband network connectivity, the home network infrastructure (including the wireless connectivity in each home network and the number of devices connected to the network), and how people in each home network use the network. Downtime is more frequent and longer in developing countries---sometimes due to the network, and in other cases because they simply turn their home router off. We also find that some portions of the wireless spectrum are extremely crowded, that diurnal patterns are more pronounced during the week, and that most traffic in home networks is exchanged over a few connections to a small number of domains. Our study is both a preliminary view into many home networks and an illustration of how measurements from a home router can yield significant information about home networks.

Software Defined Networking, or SDN, based networks are being deployed not only in testbed networks, but also in production networks. Although fault-tolerance is one of the most desirable properties in production networks, there are not much study in providing fault-tolerance to SDN-based networks. The goal of this work is to develop a fault tolerant SDN architecture that can rapidly recover from faults and scale to large network sizes. This paper presents CORONET, a SDN fault-tolerant system that recovers from multiple link failures in the data plane. We describe a prototype implementation based on NOX that demonstrates fault recovery for emulated topologies using Mininet. We also discuss possible extensions to handle control plane and controller faults.

Studying network configuration evolution can improve our understanding of the evolving complexity of networks and can be helpful in making network configuration less error-prone. Unfortunately, the nature of changes that operators make to network configuration is poorly understood. Towards improving our understanding, we examine and analyze five years of router, switch, and firewall configurations from two large campus networks using the logs from version control systems used to store the configurations. We study how network configuration is distributed across different network operations tasks and how the configuration for each task evolves over time, for different types of devices and for different locations in the network. To understand the trends of how configuration evolves over time, we study the extent to which configuration for various tasks are added, modified, or deleted. We also study whether certain devices experience configuration changes more frequently than others, as well as whether configuration changes tend to focus on specific portions of the configuration (or on specific tasks). We also investigate when network operators make configuration changes of various types. Our results concerning configuration changes can help the designers of configuration languages understand which aspects of configuration might be more automated or tested more rigorously and may ultimately help improve configuration languages.

As Internet service providers increasingly implement and impose "usage caps", consumers need better ways to help them understand and control how devices in the home use up the available network resources or available capacity. Towards this goal, we will demonstrate a system that allows users to monitor and manage their usage caps. The system uses the BISMark firmware running on network gateways to collect usage statistics and report them to a logically centralized controller, which displays usage information. The controller allows users to specify policies about how different people, devices, and applications should consume the usage cap; it implements and enforces these policies via a secure OpenFlow control channel to each gateway device. The demonstration will show various use cases, such as limiting the usage of a particular application, visualizing usage statistics, and allowing users within a single household to "trade" caps with one another.

This paper surveys our ongoing work on the use of software-defined networking to simplify two acute policy problems in campus and enterprise network operations: access control and information flow control. We describe how the current coupling of high-level policy with low-level configuration makes these problems challenging today. We describe the specific policy problems faced by campus and enterprise network operators; illustrate our approach, which leverages recent trends in separating the network's “control plane” from the data plane; and show how this approach can be applied to simplify these two enterprise network management tasks. We also describe our ongoing deployment efforts to build a campus network testbed where trial designs can be deployed and evaluated. We close with a summary of current and future research challenges for solving challenges within enterprise networks within the context of this new paradigm.

No abstract available.