Diamond sketch: Accurate per-flow measurement for real IP streams

Yang, Tong; Gao, Siang; Sun, Zhouyi; Wang, Yufei; Shen, Yulong; Li, Xiaoming

doi:10.1109/infcomw.2018.8406946

Cited by 1 publication

(5 citation statements)

References 13 publications

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“…This waste leads to space inefficiency. To solve this problem, a lot of works, such as Counter Braids [24], Counter Tree [86], Pyramid Sketch [87], OM sketch [88] and Diamond sketch [89] have designed hierarchical counter-sharing scheme to record the size of flow, as shown in Fig. 10.…”

Section: Lower Level Countermentioning

confidence: 99%

“…For more accurate. Diamond sketch [89] contains three parts: increment part, carry part, and deletion part. The increment part records the size of flow.…”

Section: ) Multi-layer Compositionmentioning

confidence: 99%

“…Counter Braids [24], Brick [92], RCC [98], Pyramid Sketch [87] CounterTree [86], OM sketch [88], ABC [97], Cold filter [60] Diamond sketch [89] multi-layer sketch Bloom Sketch [90], ASketch [133] frequency-aware updating FCM [111] TABLE X: Cardinality estimation [69] Cardinality estimation methods Std.Err. (δ) memory units…”

Section: ) Time Dimensionmentioning

confidence: 99%

“…Filter [58] Virtual Register Sharing [67] Virtual HyperLogLog [70] Counter tree [75] Braids [22] SketchVisor [135] MAPLE [160] NitroSketch [55], R HHH [54] CASE [174] CASE [183], RCC [87] HeavyGuardian [100] Sliding HLL [127] Fast sketch [120], OPA [115] Sequential hashing [112] Modular hashing [119] Bitcount [143], CBF [50] SketchLearn [20] Group Testing [159] HeavyKeeper [99] HeavyGuardian [100] ICE-Buckets [52] HyperSight [153] MultiResBitmap [170] HLL-TailCut+ [74], RAP [93], Frequent [94] ACE [125], Conservative update [88] CountMax [96], LCF [184], LR(T) [185], SpaceSaving [95], CSketch [89], CSM [90] CHHFR [171], Refined LL [166] CounterMap [198], IMP [118] SketchVisor [135], WCSS[128] SWAMP [129], RCD [113] Defeat …”

Section: Improve Accuracymentioning

confidence: 99%

“…After summarizing the counter-level optimization, the basic unit of sketch, we will get into detail of the integralsketch design. The sketch-level optimization can be divided into multi-layer composition, multi-sketch composition and Defeat [118] histogram-sketch apply the subspace method [119] to detect anomalies MRSCBF [120] SCBF [120] improve the estimate accuracy by different resolution IMP [121] Lp Sketch [122] deduce the entropy of the intersection of two traffic Sequential Hashing [123] hash table enable key reversible function RCD sketch [124] bit array reverse the host with large connection degree SF-sketch [125] CM Sketch, Count Sketch maintain a small sketch for transmission HashPipe [126] match-action hash table use P4Switch to HH detection SketchLearn [22] bit-level sketch leverage multiple bit-level Gaussian distributions Diamond sketch [89] atom sketches perform per-flow measurement on skewed traffic sliding window design. By composing a basic unit together or different sketch together, the researchers have developed novel algorithms to enlarge the sketch functionality skillfully.…”

Section: Sketch Level Optimizationmentioning

confidence: 99%

See 4 more Smart Citations

A survey of sketches in traffic measurement: Design, Optimization, Application and Implementation

Li¹,

Luo²,

Guo³

et al. 2020

Preprint

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Network measurement probes the underlying network to support upper-level decisions such as network management, network update, network maintenance, network defense and beyond. Due to the massive, speedy, unpredictable features of network flows, sketches are widely implemented in measurement nodes to record the frequency or estimate the cardinality of flows approximately. At their cores, sketches usually maintain one or multiple counter array(s), and relies on hash functions to select the counter(s) for each flow. Then the space-efficient sketches from the distributed measurement nodes are aggregated to provide statistics of the undergoing flows. Currently, tremendous redesigns and optimizations have been proposed to further improve the sketches for better network measurement performance. However, the existing reviews or surveys mainly focus on one particular aspect of measurement tasks. Researchers and engineers in the network measurement community desire an all-in-one survey which covers the whole processing pipeline of sketch-based network measurement. To this end, we present the first comprehensive survey in this area. We first introduce the preparation of flows for measurement, then detail the most recent investigations of design, aggregation, decoding, application and implementation of sketches for network measurement. To summary the existing efforts, we conduct an in-depth study of the existing literature, covering more than 80 sketch designs and optimization strategies. Furthermore, we conduct a comprehensive analysis and qualitative/quantitative comparison of the sketch designs. Finally, we highlight the open issues for future sketch-based network measurement research.

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Section: Lower Level Countermentioning

confidence: 99%

“…For more accurate. Diamond sketch [89] contains three parts: increment part, carry part, and deletion part. The increment part records the size of flow.…”

Section: ) Multi-layer Compositionmentioning

confidence: 99%

Section: ) Time Dimensionmentioning

confidence: 99%

Section: Improve Accuracymentioning

confidence: 99%

Section: Sketch Level Optimizationmentioning

confidence: 99%

See 3 more Smart Citations

A survey of sketches in traffic measurement: Design, Optimization, Application and Implementation

Li¹,

Luo²,

Guo³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

Diamond sketch: Accurate per-flow measurement for real IP streams

Cited by 1 publication

References 13 publications

A survey of sketches in traffic measurement: Design, Optimization, Application and Implementation

A survey of sketches in traffic measurement: Design, Optimization, Application and Implementation

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