Control plane compression

Beckett, Ryan; Gupta, Aarti; Mahajan, Ratul; Walker, David

doi:10.1145/3230543.3230583

Cited by 51 publications

(38 citation statements)

References 24 publications

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“…[69]- [71]), programming languages (e.g. [72]- [76]), safe network updates (e.g., [77]- [80]), data plane checking (e.g., [10], [11], [16], [25]), real-time checkers [12], [14], [30], [81], and more general network analyses (e.g., [6], [8], [9], [19], [82], [83]) together with suitable levels of abstractions (e.g., [84], [85]).…”

Section: E Discussion: Importance Of Empty Pecsmentioning

confidence: 99%

A Precise and Expressive Lattice-theoretical Framework for Efficient Network Verification

Horn

Kheradmand

Prasad

2019

2019 IEEE 27th International Conference on Network Protocols (ICNP)

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Network verification promises to detect errors, such as black holes and forwarding loops, by logically analyzing the control or data plane. To do so efficiently, the state-of-the-art (e.g., Veriflow) partitions packet headers with identical forwarding behavior into the same packet equivalence class (PEC).Recently, Yang and Lam showed how to construct the minimal set of PECs, called atomic predicates. Their construction uses Binary Decision Diagrams (BDDs). However, BDDs have been shown to incur significant overhead per packet header bit, performing poorly when analyzing large-scale data centers. The overhead of atomic predicates prompted ddNF to devise a specialized data structure of Ternary Bit Vectors (TBV) instead.However, TBVs are strictly less expressive than BDDs. Moreover, unlike atomic predicates, ddNF's set of PECs is not minimal. We show that ddNF's non-minimality is due to empty PECs. In addition, empty PECs are shown to trigger wrong analysis results. This reveals an inherent tension between precision, expressiveness and performance in formal network verification.Our paper resolves this tension through a new latticetheoretical PEC-construction algorithm, #PEC, that advances the field as follows: (i) #PEC can encode more kinds of forwarding rules (e.g., ip-tables) than ddNF and Veriflow, (ii) #PEC verifies a wider class of errors (e.g., shadowed rules) than ddNF, and (iii) on a broad range of real-world datasets, #PEC is 10× faster than atomic predicates. By achieving precision, expressiveness and performance, this paper answers a longstanding quest that has spanned three generations of formal network analysis techniques.

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Section: E Discussion: Importance Of Empty Pecsmentioning

confidence: 99%

A Precise and Expressive Lattice-theoretical Framework for Efficient Network Verification

Horn

Kheradmand

Prasad

2019

2019 IEEE 27th International Conference on Network Protocols (ICNP)

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“…Kollaps only deals with data-plane, and it is hence complementary to CrystalNet. To enhance the efficiency of large-scale control-plane analysis, Bonsai [27] leveraged the idea of network compression while persevering the network properties. Kollaps' network collapsing achieves a similar goal, however, it targets a different set of network properties, again in the data-plane.…”

Section: Related Workmentioning

confidence: 99%

“…Static topology. element, either for changes to link properties (lines 21-23) or for addition and removal of services, links and bridges (lines [24][25][26][27][28][29][30][31][32][33][34][35][36]. A dedicated DSL to easily program more complex dynamic patterns on top of Kollaps is detailed in [55].…”

Section: The Kollaps Systemmentioning

confidence: 99%

Kollaps

Gouveia¹,

Neves²,

Segarra

et al. 2020

Proceedings of the Fifteenth European Conference on Computer Systems

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The performance and behavior of large-scale distributed applications is highly influenced by network properties such as latency, bandwidth, packet loss, and jitter. For instance, an engineer might need to answer questions such as: What is the impact of an increase in network latency in application response time? How does moving a cluster between geographical regions affect application throughput? How network dynamics affects application stability? Answering these questions in a systematic and reproducible way is very hard, given the variability and lack of control over the underlying network. Unfortunately, state-of-the-art network emulation or testbeds scale poorly (i.e., MiniNet), focus exclusively on the control-plane (i.e., CrystalNet) or ignore network dynamics (i.e., EmuLab).Kollaps is a fully distributed network emulator that address these limitations. Kollaps hinges on two key observations. First, from an application's perspective, what matters are the emergent end-to-end properties (e.g., latency, bandwidth, packet loss, and jitter) rather than the internal state of the routers and switches leading to those properties. This premise allows us to build a simpler, dynamically adaptable, emulation model that circumvent maintaining the full network state. Second, this simplified model is maintainable in a fully decentralized way, allowing the emulation to scale with the number of machines for the application.Kollaps is fully decentralized, agnostic of the application language and transport protocol, scales to thousands of processes and is accurate when compared against a bare-metal deployment or state-of-the-art approaches that emulate the full state of the network. We showcase how Kollaps can accurately reproduce results from the literature and predict * Corresponding author † Corresponding author the behaviour of a complex unmodified distributed key-value store (i.e., Cassandra) under different deployments.also arise in the reproducibility crisis currently plaguing the system's community [29,64]. Different results for the same system emerge not only because systems are evaluated in different uncontrollable conditions, but also because research testbeds such as Emulab [46], CloudLab [72], or PlanetLab [35] used to conduct experiments tend to get overloaded right before system conference deadlines [51]. We therefore need tools to systematically assess and reproduce the evaluation of large-scale applications. Notably, similar tools could be used to test and (otherwise) prevent costly incidents due to mis-configurations, as in recent events [4,20]. One approach to systematically evaluate a large-scale distributed system is to resort to simulation, which relies on models that capture the key properties of the target system and environment [25]. Simulation provides full control of the system and environment -achieving full reproducibility -and allows to study the model of the system in a variety of scenarios. However, simulations suffer from several well-known problems. In fact, there is a large gap between the...

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“…To enable scalable analyses, it seems necessary to exploit the symmetries that exist in most large real networks. Indeed, other researchers have exploited symmetries to scale verification in the past [3,22]. However, it has never been possible to account for failures, as they introduce asymmetries that change routing behaviors in unpredictable ways.…”

Section: Introductionmentioning

confidence: 99%

“…To address this challenge, we develop a new algorithm for verifying reachability in networks in the presence of faults, based on the idea of counterexampleguided abstraction refinement (CEGAR) [5]. The algorithm starts by factoring out symmetries using techniques developed in prior work [3] and then attempts verification of the abstract network using an SMT solver. If verification succeeds, we are done.…”

Section: Introductionmentioning

confidence: 99%

Efficient Verification of Network Fault Tolerance via Counterexample-Guided Refinement

Giannarakis

Beckett

Mahajan

et al. 2019

Computer Aided Verification

Self Cite

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We show how to verify that large data center networks satisfy key properties such as all-pairs reachability under a bounded number of faults. To scale the analysis, we develop algorithms that identify network symmetries and compute small abstract networks from large concrete ones. Using counterexample guided abstraction refinement, we successively refine the computed abstractions until the given property may be verified. The soundness of our approach relies on a novel notion of network approximation: routing paths in the concrete network are not precisely simulated by those in the abstract network but are guaranteed to be "at least as good." We implement our algorithms in a tool called Origami and use them to verify reachability under faults for standard data center topologies. We find that Origami computes abstract networks with 1-3 orders of magnitude fewer edges, which makes it possible to verify large networks that are out of reach of existing techniques.

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Control plane compression

Cited by 51 publications

References 24 publications

A Precise and Expressive Lattice-theoretical Framework for Efficient Network Verification

A Precise and Expressive Lattice-theoretical Framework for Efficient Network Verification

Kollaps

Efficient Verification of Network Fault Tolerance via Counterexample-Guided Refinement

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