Synchronized network snapshots

Yaseen, Nofel; Sonchack, John; Liu, Vincent

doi:10.1145/3230543.3230552

Cited by 29 publications

(14 citation statements)

References 22 publications

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“…(Exp#4) Consistency. We evaluate epoch synchronization by comparing OmniMon with the open-source Speedlight (SPL) prototype [73], a synchronization solution for network telemetry. We adapt the SPL prototype, written in P4 BMv2 [55], to our Tofino switches.…”

Section: Resultsmentioning

confidence: 99%

“…• It completes controller operations in limited time even with 10 6 flows (Exp#3). • It incurs less switch resource usage and smaller epoch boundary deviations than Speedlight [73], a synchronization solution for network telemetry (Exp#4). • It infers 99.7% of packet losses in an extreme case (Exp#5).…”

Section: Discussionmentioning

confidence: 99%

“…Weak consistency synchronizes per-packet epochs in a best-effort manner, so the epochs among entities may diverge and a packet may be monitored at different epochs. In weak consistency, each entity renews its epoch via a local clock and embeds the current epoch in every outgoing packet (as in Lamport Clock [36] and Distributed Snapshots [13,73]). When an entity receives a packet, it updates its current epoch with the packet's embedded epoch if the latter is newer.…”

Section: Challengementioning

confidence: 99%

“…Swing State [44] ensures consistent data-plane states via state migration, but poses heavy state update costs. Synchronized Network Snapshot [73] realizes causal consistency (a form of weak consistency) in P4 switches. OmniMon achieves hybrid consistency and is more lightweight (Exp#4 in §8).…”

Section: Related Workmentioning

confidence: 99%

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OmniMon

Huang

Sun

Lee

et al. 2020

Proceedings of the Annual Conference of the ACM Special Interest Group on Data Communication on the Applications, Technologies,

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Network telemetry is essential for administrators to monitor massive data traffic in a network-wide manner. Existing telemetry solutions often face the dilemma between resource efficiency (i.e., low CPU, memory, and bandwidth overhead) and full accuracy (i.e., error-free and holistic measurement). We break this dilemma via a network-wide architectural design OmniMon, which simultaneously achieves resource efficiency and full accuracy in flow-level telemetry for large-scale data centers. OmniMon carefully coordinates the collaboration among different types of entities in the whole network to execute telemetry operations, such that the resource constraints of each entity are satisfied without compromising full accuracy. It further addresses consistency in network-wide epoch synchronization and accountability in error-free packet loss inference. We prototype OmniMon in DPDK and P4. Testbed experiments on commodity servers and Tofino switches demonstrate the effectiveness of OmniMon over state-of-the-art telemetry designs. CCS CONCEPTS • Networks → Network measurement.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Challengementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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OmniMon

Huang

Sun

Lee

et al. 2020

Proceedings of the Annual Conference of the ACM Special Interest Group on Data Communication on the Applications, Technologies,

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“…Researchers have looked at accelerating various applications in networking and distributed sys-tems using programmable switches. Examples include layer-4 load balancing [51], [58], network resource allocation mechanisms [69], key-value stores [35], [48], coordination services [34], [46], [47], fair queueing [70], fast connectivity recovery [32], network monitoring and measurement tasks [73], [55], [74], [75], [30], [87]. These applications achieve far better performance with lower costs than their software counterparts that run on commodity servers.…”

Section: Related Workmentioning

confidence: 99%

Poseidon: Mitigating Volumetric DDoS Attacks with Programmable Switches

Zhang

Wang

et al. 2020

Proceedings 2020 Network and Distributed System Security Symposium

139

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Distributed Denial-of-Service (DDoS) attacks have become a critical threat to the Internet. Due to the increasing number of vulnerable Internet of Things (IoT) devices, attackers can easily compromise a large set of nodes and launch highvolume DDoS attacks from the botnets. State-of-the-art DDoS defenses, however, have not caught up with the fast development of the attacks. Middlebox-based defenses can achieve high performance with specialized hardware; however, these defenses incur a high cost, and deploying new defenses typically requires a device upgrade. On the other hand, software-based defenses are highly flexible, but software-based packet processing leads to high performance overheads. In this paper, we propose POSEIDON, a system that addresses these limitations in today's DDoS defenses. It leverages emerging programmable switches, which can be reconfigured in the field without additional hardware upgrade. Users of POSEIDON can specify their defense strategies in a modular fashion in the form of a set of defense primitives; this can be further customized easily for each network and extended to include new defenses. POSEIDON then maps the defense primitives to run on programmable switches-and when necessary, on server software-for effective defense. When attacks change, POSEIDON can reconfigure the underlying defense primitives to respond to the new attack patterns. Evaluations using our prototype demonstrate that POSEIDON can effectively defend against highvolume attacks, easily support customization of defense strategies, and adapt to dynamic attacks with low overheads.

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Introduction

2021

Network Behavior Analysis

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As the Internet continues to grow in devices, applications, services, users, and traffic, network behavior analysis has increasingly become a crucial area in research for understanding what is happening on the Internet. This chapter first introduces the definition and concept of network behavior and discusses the importance and urgent need of network behavior analysis. Subsequently, this chapter describes the common methods, infrastructure, and frameworks for collecting, monitoring, modeling and analyzing network behavior. Next, this chapter discusses the broad benefits and applications of exploring network behavior in behavioral profiling, anomaly detection, traffic engineering, and security monitoring. Finally, this chapter concludes with an overview of the topics covered in this book and the overall organization of the book chapters. What is Network Behavior AnalysisAs the Internet continues to grow in size and complexity, the challenge of effectively provisioning, managing, and securing it has become inextricably linked to a deep understanding of Internet traffic and network behavior [1][2][3][4][5]. The imperative and urgency of understanding network behavior and traffic patterns gives rise to the field of network behavior analysis (NBA), which focuses on the study of network traffic data for providing critical insights into behavioral patterns of end systems and network applications.Throughout this book, the terms, networked systems, end hosts, and end systems, will be used interchangeably for representing all Internet-connected devices including desktops, laptops, tablets, smartphones, servers as well as Internet of Thing (IoT) devices. We will also use the terms, Internet applications, network applications, and application services interchangeably for denoting the broad range of applications and services running on the Internet infrastructure, such as e-mail, web, video and music streaming, gaming, online social media, and smartphone apps.

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Synchronized network snapshots

Cited by 29 publications

References 22 publications

OmniMon

OmniMon

Poseidon: Mitigating Volumetric DDoS Attacks with Programmable Switches

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