The Networked Systems Group (NSG) is a research group in the Department of Information Technology and Electrical Engineering (D-ITET) at ETH Zürich led by Prof. Laurent Vanbever.
Our research interests are centered around complex network management problems, with the larger goal of making current and future networks (especially the Internet) easier to design, understand and operate. We are currently active in multiple areas including network programmability, data-driven networking, verification, routing, and security. Most of our projects are inherently multidisciplinary and tend to involve recent advances in programming languages, algorithmics, and machine learning.
We are active in several areas, a subset of which include:
Our flagship lecture is Communication Networks offered in the Spring semester. We also offer a (master-evel) seminar in the Spring semester, and a lecture on Advanced Topics in Communication Networks in the Fall semester. Check our courses page for more information.
You can learn more about our recent research and teaching activities in our latest activity report.
Maria Apostolaki (2nd PhD student to graduate from NSG) will join Princeton's Electrical and Computer Engineering Department (ECE) as a tenure-track assistant professor in the Fall of 2022! Huge congrats to her!!
Laurent will co-chair ACM HotNets 2021 (the ACM Workshop on Hot Topics in Networks) together with Prof. Minlan Yu from Harvard.
2020 has been a really "particular" year—including for our research group. And yet, despite everything, 2020 was also a good year for us. Check out our latest activity report to learn more about our research and teaching activities (and successes) in 2020.
USENIX NSDI 2021. Online (April 2021).
Network analysis and verification tools are often a godsend for network operators as they free them from the fear of introducing outages or security breaches. As with any complex software though, these tools can (and often do) have bugs. For the operators, these bugs are not necessarily problematic except if they affect the precision of the model as it applies to their specific network. In that case, the tool output might be wrong: it might fail to detect actual configuration errors and/or report non-existing ones.
In this paper, we present Metha, a framework that systematically tests network analysis and verification tools for bugs in their network models. Metha automatically generates syntactically- and semantically-valid configurations; compares the tool’s output to that of the actual router software; and detects any discrepancy as a bug in the tool’s model. The challenge in testing network analyzers this way is that a bug may occur very rarely and only when a specific set of configuration statements is present. We address this challenge by leveraging grammar-based fuzzing together with combinatorial testing to ensure thorough coverage of the search space and by identifying the minimal set of statements triggering the bug through delta debugging.
We implemented Metha and used it to test three well-known tools. In all of them, we found multiple (new) bugs in their models, most of which were confirmed by the developers.
Thomas Wirtgen, Quentin De Coninck, Randy Bush, Laurent Vanbever, Olivier Bonaventure
ACM HotNets 2020. Chicago, Illinois, USA (November 2020).
Thanks to the standardization of routing protocols such as BGP, OSPF or IS-IS, Internet Service Providers (ISP) and enterprise networks can deploy routers from various vendors. This prevents them from vendor-lockin problems. Unfortunately, this also slows innovation since any new feature must be standardized and implemented by all vendors before being deployed.
We propose a paradigm shift that enables network operators to program the routing protocols used in their networks. We demonstrate the feasibility of this approach with xBGP. xBGP is a vendor neutral API that exposes the key data structures and functions of any BGP implementation. Each xBGP compliant implementation includes an eBPF virtual machine that executes the operator supplied programs. We extend FRRouting and BIRD to support this new paradigm and demonstrate the flexibility of xBGP with four different use cases. Finally, we discuss how xBGP could affect future research on future routing protocols.
ACM HotNets 2020. Chicago, Illinois, USA (November 2020).
Programmable devices allow the operator to specify the data-plane behavior of a network device in a high-level language such as P4. The compiler then maps the P4 program to the hardware after applying a set of optimizations to minimize resource utilization. Yet, the lack of context restricts the compiler to conservatively account for all possible inputs -- including unrealistic or infrequent ones -- leading to sub-optimal use of the resources or even compilation failures. To address this inefficiency, we propose that the compiler leverages insights from actual traffic traces, effectively unlocking a broader spectrum of possible optimizations.
We present a system working alongside the compiler that uses traffic-awareness to reduce the allocated resources of a P4 program by: (i) removing dependencies that do not manifest; (ii) adjusting table and register sizes to reduce the pipeline length; and (iii) offloading parts of the program that are rarely used to the controller. Our prototype implementation on the Tofino switch automatically profiles the P4 program, detects opportunities and performs optimizations to improve the pipeline efficiency.
Our work showcases the potential benefit of applying profiling techniques used to compile general-purpose languages to compiling P4 programs.
Samuel Steffen, Timon Gehr, Petar Tsankov, Laurent Vanbever, Martin Vechev
ACM SIGCOMM 2020. New York, USA (August 2020).
Not all important network properties need to be enforced all the time. Often, what matters instead is the fraction of time / probability these properties hold. Computing the probability of a property in a network relying on complex inter-dependent routing protocols is challenging and requires determining all failure scenarios for which the property is violated. Doing so at scale and accurately goes beyond the capabilities of current network analyzers.
In this paper, we introduce NetDice, the first scalable and accurate probabilistic network configuration analyzer supporting BGP, OSPF, ECMP, and static routes. Our key contribution is an inference algorithm to efficiently explore the space of failure scenarios. More specifically, given a network configuration and a property phi, our algorithm automatically identifies a set of links whose failure is provably guaranteed not to change whether phi holds. By pruning these failure scenarios, NetDice manages to accurately approximate P(phi). NetDice supports practical properties and expressive failure models including correlated link failures.
We implement NetDice and evaluate it on realistic configurations. NetDice is practical: it can precisely verify probabilistic properties in few minutes, even in large networks.
ACM SIGCOMM CCR 2020. Volume 50 Issue 2 (April 2020).
Each year at ETH Zurich, around 100 students collectively build and operate their very own Internet infrastructure composed of hundreds of routers and dozens of Autonomous Systems (ASes). Their goal? Enabling Internet-wide connectivity.
We find this class-wide project to be invaluable in teaching our students how the Internet infrastructure practically works. Among others, our students have a much deeper understanding of Internet operations alongside their pitfalls. Besides students tend to love the project: clearly the fact that all of them need to cooperate for the entire Internet to work is empowering.
In this paper, we describe the overall design of our teaching platform, how we use it, and interesting lessons we have learnt over the years. We also make our platform openly available.
USENIX NSDI 2020. Santa Clara, California, USA (February 2020).
Network verification and configuration synthesis are promising approaches to make networks more reliable and secure by enforcing a set of policies. However, these approaches require a formal and precise description of the intended network behavior, imposing a major barrier to their adoption: network operators are not only reluctant to write formal specifications, but often do not even know what these specifications are.
We present Config2Spec, a system that automatically synthesizes a formal specification (a set of policies) of a network given its configuration and a failure model (e.g., up to two link failures). A key technical challenge is to design a synthesis algorithm which can efficiently explore the large space of possible policies. To address this challenge, Config2Spec relies on a careful combination of two well-known methods: data plane analysis and control plane verification.
Experimental results show that Config2Spec scales to mining specifications of large networks (>150 routers).
USENIX NSDI 2020. Santa Clara, California, USA (February 2020).
Push-In First-Out (PIFO) queues are hardware primitives which enable programmable packet scheduling by allowing to perfectly reorder packets at line rate. While promising, implementing PIFO queues in hardware and at scale is not easy: only hardware designs (not implementations) exist and they can only support about 1000 flows.
In this paper, we introduce SP-PIFO, a programmable packet scheduler which closely approximates the behavior of PIFO queues using strict-priority queues—at line rate, at scale, and on existing devices. The key insight behind SP-PIFO is to dynamically adapt the mapping between packet ranks and available queues to minimize the scheduling errors. We present a mathematical formulation of the problem and derive an adaptation technique which closely approximates the optimal queue mapping without any traffic knowledge.
We fully implement SP-PIFO in P4 and evaluate it on real workloads. We show that SP-PIFO: (i) closely matches ideal PIFO performance, with as little as 8 priority queues; (ii) arbitrarily scales to large amount of flows and ranks; and (iii) quickly adapts to traffic variations. We also show that SP-PIFO runs at line rate on existing programmable data planes.
ACM HotNets 2019. Princeton, NJ, USA (November 2019).
Traditional network control planes can be slow and require manual tinkering from operators to change their behavior. There is thus great interest in a faster, data-driven approach that uses signals from real-time traffic instead. However, the promise of fast and automatic reaction to data comes with new risks: malicious inputs designed towards negative outcomes for the network, service providers, users, and operators.
Adversarial inputs are a well-recognized problem in other areas; we show that networking applications are susceptible to them too. We characterize the attack surface of data-driven networks and examine how attackers with different privileges—from infected hosts to operator-level access—may target network infrastructure, applications, and protocols. To illustrate the problem, we present case studies with concrete attacks on recently proposed data-driven systems.
Our analysis urgently calls for a careful study of attacks and defenses in data-driven networking, with a view towards ensuring that their promise is not marred by oversights in robust design.
USENIX NSDI 2019. Boston, Massachusetts, USA (February 2019).
We present Blink, a data-driven system that leverages TCP-induced signals to detect failures directly in the data plane. The key intuition behind Blink is that a TCP flow exhibits a predictable behavior upon disruption: retransmitting the same packet over and over, at epochs exponentially spaced in time. When compounded over multiple flows, this behavior creates a strong and characteristic failure signal. Blink efficiently analyzes TCP flows to: (i) select which ones to track; (ii) reliably and quickly detect major traffic disruptions; and (iii) recover connectivity---all this, completely in the data plane. We present an implementation of Blink in P4 together with an extensive evaluation on real and synthetic traffic traces. Our results indicate that Blink: (i) achieves sub-second rerouting for large fractions of Internet traffic; and (ii) prevents unnecessary traffic shifts even in the presence of noise. We further show the feasibility of Blink by running it on an actual Tofino switch.
NDSS Symposium 2019. San Diego, CA, USA (February 2019).
Nowadays Internet routing attacks remain practically effective as existing countermeasures either fail to provide protection guarantees or are not easily deployable. Blockchain systems are particularly vulnerable to such attacks as they rely on Internet-wide communications to reach consensus. In particular, Bitcoin---the most widely-used cryptocurrency---can be split in half by any AS-level adversary using BGP hijacking.
In this paper, we present SABRE, a secure and scalable Bitcoin relay network which relays blocks worldwide through a set of connections that are resilient to routing attacks. SABRE runs alongside the existing peer-to-peer network and is easily deployable. As a critical system, SABRE design is highly resilient and can efficiently handle high bandwidth loads, including Denial of Service attacks.
We built SABRE around two key technical insights. First, we leverage fundamental properties of inter-domain routing (BGP) policies to host relay nodes: (i) in networks that are inherently protected against routing attacks; and (ii) on paths that are economically-preferred by the majority of Bitcoin clients. These properties are generic and can be used to protect other Blockchain-based systems. Second, we leverage the fact that relaying blocks is communication-heavy, not computation-heavy. This enables us to offload most of the relay operations to programmable network hardware (using the P4 programming language). Thanks to this hardware/software co-design, SABRE nodes operate seamlessly under high load while mitigating the effects of malicious clients.
We present a complete implementation of SABRE together with an extensive evaluation. Our results demonstrate that SABRE is effective at securing Bitcoin against routing attacks, even with deployments of as few as 6 nodes.
ACM HotNets 2018. Redmond, WA, USA (November 2018).
One design principle of modern network architecture seems to be set in stone: a software-based control plane drives a hardware- or software-based data plane. We argue that it is time to revisit this principle after the advent of programmable switch ASICs which can run complex logic at line rate.
We explore the possibility and benefits of accelerating the control plane by offloading some of its tasks directly to the network hardware. We show that programmable data planes are indeed powerful enough to run key control plane tasks including: failure detection and notification, connectivity retrieval, and even policy-based routing protocols. We implement in P4 a prototype of such a “hardware-accelerated” control plane, and illustrate its benefits in a case study.
Despite such benefits, we acknowledge that offloading tasks to hardware is not a silver bullet. We discuss its tradeoffs and limitations, and outline future research directions towards hardware-software codesign of network control planes.
USENIX Security 2018. Baltimore, MD, USA (August 2018).
Simple path tracing tools such as traceroute allow malicious users to infer network topologies remotely and use that knowledge to craft advanced denial-of-service (DoS) attacks such as Link-Flooding Attacks (LFAs). Yet, despite the risk, most network operators still allow path tracing as it is an essential network debugging tool.
In this paper, we present NetHide, a network topology obfuscation framework that mitigates LFAs while preserving the practicality of path tracing tools. The key idea behind NetHide is to formulate network obfuscation as a multi-objective optimization problem that allows for a flexible tradeoff between security (encoded as hard constraints) and usability (encoded as soft constraints). While solving this problem exactly is hard, we show that NetHide can obfuscate topologies at scale by only considering a subset of the candidate solutions and without reducing obfuscation quality. In practice, NetHide obfuscates the topology by intercepting and modifying path tracing probes directly in the data plane. We show that this process can be done at line-rate, in a stateless fashion, by leveraging the latest generation of programmable network devices.
We fully implemented NetHide and evaluated it on realistic topologies. Our results show that NetHide is able to obfuscate large topologies (> 150 nodes) while preserving near-perfect debugging capabilities. In particular, we show that operators can still precisely trace back > 90% of link failures despite obfuscation.
Timon Gehr, Sasa Misailovic, Petar Tsankov, Laurent Vanbever, Pascal Wiesman, Martin Vechev
PLDI 2018. Philadelphia, Pennsylvania, USA (June 2018).
Network operators often need to ensure that important probabilistic properties are met, such as that the probability of network congestion is below a certain threshold. Ensuring such properties is challenging and requires both a suitable language for probabilistic networks and an automated procedure for answering probabilistic inference queries. We present Bayonet, a novel approach that consists of: (i) a probabilistic network programming language and (ii) a system that performs probabilistic inference on Bayonet programs. The key insight behind Bayonet is to phrase the problem of probabilistic network reasoning as inference in existing probabilistic languages. As a result, Bayonet directly leverages existing probabilistic inference systems and offers a flexible and expressive interface to operators. We present a detailed evaluation of Bayonet on common network scenarios, such as network congestion, reliability of packet delivery, and others. Our results indicate that Bayonet can express such practical scenarios and answer queries for realistic topology sizes (with up to 30 nodes).
USENIX NSDI 2018. Renton, Washington, USA (April 2018).
For an Internet Service Provider (ISP), getting an accurate picture of how its network behaves is challenging. Indeed, given the carried traffic volume and the impossibility to control end-hosts, ISPs often have no other choice but to rely on heavily sampled traffic statistics, which provide them with coarse-grained visibility at a less than ideal time resolution (seconds or minutes). We present Stroboscope, a system that enables fine-grained monitoring of any traffic flow by instructing routers to mirror millisecond-long traffic slices in a programmatic way. Stroboscope takes as input high-level monitoring queries together with a budget and automatically determines: (i) which flows to mirror; (ii) where to place mirroring rules, using fast and provably correct algorithms; and (iii) when to schedule these rules to maximize coverage while meeting the input budget. We implemented Stroboscope, and show that it scales well: it computes schedules for large networks and query sizes in few seconds, and produces a number of mirroring rules well within the limits of current routers. We also show that Stroboscope works on existing routers and is therefore immediately deployable.