Nitrosketch

Liu, Zaoxing; Ben-Basat, Ran; Einziger, Gil; Kassner, Yaron; Braverman, Vladimir; Friedman, Roy; Sekar, Vyas

doi:10.1145/3341302.3342076

Cited by 142 publications

(18 citation statements)

References 63 publications

(71 reference statements)

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“…SketchLearn [9] separates large flows from the rest based on inferred flow distributions, which incurs additional processing delay for each packet. Nitrosketch [16] reduces the insertion frequency to relieve the processing delay, but introduces uncertainty on the sketching results. OmniMon [17] seeks full accuracy and resource efficiency over collaborated network entities.…”

Section: B Related Workmentioning

confidence: 99%

A One-Pass Clustering Based Sketch Method for Network Monitoring

Shen

et al. 2023

IEEE/ACM Trans. Networking

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Network monitoring solutions need to cope with increasing network traffic volumes, as a result, sketch-based monitoring methods have been extensively studied to trade accuracy for memory scalability and storage reduction. However, sketches are sensitive to skewness in network flow distributions due to hash collisions, and need complicated performance optimization to adapt to line-rate packet streams.We provide Jellyfish, an efficient sketch method that performs one-pass clustering over the network stream. One-pass clustering is realized by adapting the monitoring granularity from the whole network flow to fragments called subflows, which not only reduces the ingestion rate but also provides an efficient intermediate representation for the input to the sketch. Jellyfish provides the network-flow level query interface by reconstructing the networkflow level counters by merging subflow records from the same network flow. We provide probabilistic analysis of the expected accuracy of both existing sketch methods and Jellyfish. Realworld trace-driven experiments show that Jellyfish reduces the average estimation errors by up to six orders of magnitude for per-flow queries, by six orders of magnitude for entropy queries, and up to ten times for heavy-hitter queries.

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Section: B Related Workmentioning

confidence: 99%

A One-Pass Clustering Based Sketch Method for Network Monitoring

Shen

et al. 2023

IEEE/ACM Trans. Networking

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“…ORAM storage for flow statistics and dedicated oblivious primitives for specific measurement logic together can make access patterns of measurement tasks oblivious [51]. Though OblivSketch [51] handles a typical link (40 Kpps, packets per second) with the same characteristic as a backbone link [52], a 10 Gbps line rate with maximum 14.88 Mpps [53] makes the measurement service hard to work with, not to mention monitoring a large-scale network with heavy traffic volume or burst traffic, where more than one switch is deployed.…”

Section: Available Solutions and Limitationsmentioning

confidence: 99%

“…There have been considerable efforts [35][36][37]53,55,56,62,63] in making a reasonable trade-off among goals: achieving a higher line-rate (such as, 40 Gbps) for 64B packets (59.2 Mpps), providing better accuracy guarantees (than CM sketch), saving more memory space, or supporting more general measurement tasks. Thanks to their higher accuracy than sampling methods [57,58], most of them use sketches [54] as their primary data structure to record flow statistics.…”

Section: Non-oblivious Network Measurement Servicementioning

confidence: 99%

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FO-Sketch: A Fast Oblivious Sketch for Secure Network Measurement Service in the Cloud

et al. 2021

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Network measurements are the foundation for network applications. The metrics generated by those measurements help applications improve their performance of the monitored network and harden their security. As severe network attacks using leaked information from a public cloud exist, it raises privacy and security concerns if directly deployed in network measurement services in a third-party public cloud infrastructure. Recent studies, most notably OblivSketch, demonstrated the feasibility of alleviating those concerns by using trusted hardware and Oblivious RAM (ORAM). As their performance is not good enough, and there are certain limitations, they are not suitable for broad deployment. In this paper, we propose FO-Sketch, a more efficient and general network measurement service that meets the most stringent security requirements, especially for a large-scale network with heavy traffic volume and burst traffic. Let a mergeable sketch update the local flow statistics in each local switch; FO-Sketch merges (in an Intel SGX-created enclave) these sketches obliviously to form a global “one big sketch” in the cloud. With the help of Oblivious Shuffle, Divide and Conquer, and SIMD speedup, we optimize all of the critical routines in our FO-Sketch to make it 17.3x faster than a trivial oblivious solution. While keeping the same level of accuracy and packet processing throughput as non-oblivious Elastic Sketch, our FO-Sketch needs only ∼ 4.5MB enclave memory space in total to record metrics and for PORAM to store the global sketch in the cloud. Extensive experiments demonstrate that, for the recommended setting, it takes only ∼ 0.6s in total to rebuild those data during each measurement interval.

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“…An elastic NetCache application may be constructed from an elastic count-min sketch and an elastic key-value store. To control the relative stretch of these modules, the programmer can specify a utility function that the Key-value store/ hash table Precision [1], Sonata [8], Network-Wide HH [9], Sketchvisor [11], LinearRoad [13], NetChain [14], NetCache [15], FlowRadar [18], HashPipe [28], Elastic Sketch [31] Hash-based matrix SketchVisor [11], NetCache [15], Nitrosketch [19], UnivMon [20], Sharma et al [25], Fair Queueing [26], Elastic Sketch [31]…”

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confidence: 99%

Elastic Switch Programming with P4All

Hogan

Landau-Feibish

Arashloo

et al. 2020

Proceedings of the 19th ACM Workshop on Hot Topics in Networks

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The P4 language enables a range of new network applications. However, it is still far from easy to implement and optimize P4 programs for PISA hardware. Programmers must engage in a tedious "trial and error" process wherein they write their program (guessing it will fit within the hardware) and then check by compiling it. If it fails, they repeat the process. In this paper, we argue that programmers should define elastic data structures that stretch automatically to make use of available switch resources. We present P4All, an extension of P4 that supports elastic switch programming. Elastic data structures also make P4All modules reusable across different applications and hardware targets, where resource needs and constraints may vary. Our design is oriented around use of symbolic primitives (integers that may take on a range of possible values at compile time), arrays, and loops. We show how to use these primitive mechanisms to build a range of reusable libraries such as hash tables, Bloom filters, sketches, and key-value stores. We also explain the important role that elasticity plays in modular programming, and we allow programmers to declare utility functions that control the relative share of data-plane resources apportioned to each module.

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Nitrosketch

Cited by 142 publications

References 63 publications

A One-Pass Clustering Based Sketch Method for Network Monitoring

A One-Pass Clustering Based Sketch Method for Network Monitoring

FO-Sketch: A Fast Oblivious Sketch for Secure Network Measurement Service in the Cloud

Elastic Switch Programming with P4All

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