Don't thrash

Bender, Michael A.; Farach-Colton, Martı́n; Johnson, Rob; Kraner, Russell; Kuszmaul, Bradley C.; Medjedovic, Dzejla; Montes, Pablo; Shetty, Pradeep; Spillane, Richard P.; Zadok, Erez

doi:10.14778/2350229.2350275

Cited by 152 publications

(35 citation statements)

References 14 publications

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“…The counting quotient filter represents S by storing a compact, lossless representation of the multiset h(S), where h : U/f0; .2 p À 1g is a hash function and U is the universe from which S is drawn. The CQF sets p = log 2 ðn=dÞ to obtain a false-positive rate while handling up to n insertions (Bender et al, 2012).…”

Section: Declaration Of Interestsmentioning

confidence: 99%

Mantis: A Fast, Small, and Exact Large-Scale Sequence-Search Index

et al. 2018

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Sequence-level searches on large collections of RNA sequencing experiments, such as the NCBI Sequence Read Archive (SRA), would enable one to ask many questions about the expression or variation of a given transcript in a population. Existing approaches, such as the sequence Bloom tree, suffer from fundamental limitations of the Bloom filter, resulting in slow build and query times, less-than-optimal space usage, and potentially large numbers of false-positives. This paper introduces Mantis, a space-efficient system that uses new data structures to index thousands of raw-read experiments and facilitates large-scale sequence searches. In our evaluation, index construction with Mantis is 6× faster and yields a 20% smaller index than the state-of-the-art split sequence Bloom tree (SSBT). For queries, Mantis is 6-108× faster than SSBT and has no false-positives or -negatives. For example, Mantis was able to search for all 200,400 known human transcripts in an index of 2,652 RNA sequencing experiments in 82 min; SSBT took close to 4 days.

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Section: Declaration Of Interestsmentioning

confidence: 99%

Mantis: A Fast, Small, and Exact Large-Scale Sequence-Search Index

et al. 2018

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“…However, limiting keys to 31 bits can be a restricting factor for some (where larger keys are required). In the future, we will focus on allowing wider key spans, either by separating the lock-bit from the rest of the key (sacrifices performance), or through a hierarchical structure and grouping a set of elements together so that they share the same key (e.g., like in quotient filters [7] or lifted B-trees [42]).…”

Section: Resultsmentioning

confidence: 99%

Engineering a high-performance GPU B-Tree

Awad

Ashkiani

Johnson

et al. 2019

Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming

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We engineer a GPU implementation of a B-Tree that supports concurrent queries (point, range, and successor) and updates (insertions and deletions). Our B-tree outperforms the state of the art, a GPU log-structured merge tree (LSM) and a GPU sorted array. In particular, point and range queries are significantly faster than in a GPU LSM (the GPU LSM does not implement successor queries). Furthermore, B-Tree insertions are also faster than LSM and sorted array insertions unless insertions come in batches of more than roughly 100k. Because we cache the upper levels of the tree, we achieve lookup throughput that exceeds the DRAM bandwidth of the GPU. We demonstrate that the key limiter of performance on a GPU is contention and describe the design choices that allow us to achieve this high performance.

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“…The bloom filter (BF), cuckoo filter (CF) and quotient filter (QF) are three different types of space-efficient probabilistic data structures that are used to check whether an element is a member of a massive dataset or not. According to [19], QFs have faster and efficient querying of the elements than BFs, even in secondary memory. Authors in [20] stated that CFs have better practically performance than BFs and QFs.…”

Section: Type Of Node Detectionmentioning

confidence: 99%

A Trust Model Using Edge Nodes and a Cuckoo Filter for Securing VANET under the NLoS Condition

et al. 2020

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Trust, as a key element of security, has a vital role in securing vehicular ad-hoc networks (VANETs). Malicious and selfish nodes by generating inaccurate information, have undesirable impacts on the trustworthiness of the VANET environment. Obstacles also have a negative impact on data trustworthiness by restricting direct communication between nodes. In this study, a trust model based on plausibility, experience, and type of vehicle is presented to cope with inaccurate, incomplete and uncertainty data under both line of sight (LoS) and none-line of sight (NLoS) conditions. In addition, a model using the k-nearest neighbor (kNN) classification algorithm based on feature similarity and symmetry is developed to detect the NLoS condition. Radio signal strength indicator (RSSI), packet reception rate (PDR) and the distance between two vehicle nodes are the features used in the proposed kNN algorithm. Moreover, due to the big data generated in VANET, secure communication between vehicle and edge node is designed using the Cuckoo filter. All obtained results are validated through well-known evaluation measures such as precision, recall, overall accuracy, and communication overhead. The results indicate that the proposed trust model has a better performance as compared to the attack-resistant trust management (ART) scheme and weighted voting (WV) approach. Additionally, the proposed trust model outperforms both ART and WV approaches under different patterns of attack such as a simple attack, opinion tampering attack, and cunning attack. Monte-Carlo simulation results also prove validity of the proposed trust model.

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Don't thrash

Cited by 152 publications

References 14 publications

Mantis: A Fast, Small, and Exact Large-Scale Sequence-Search Index

Mantis: A Fast, Small, and Exact Large-Scale Sequence-Search Index

Engineering a high-performance GPU B-Tree

A Trust Model Using Edge Nodes and a Cuckoo Filter for Securing VANET under the NLoS Condition

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