research-article

DLSH: a distribution-aware LSH scheme for approximate nearest neighbor query in cloud computing

Authors:

Pengfei ZuoAuthors Info & Claims

SoCC '17: Proceedings of the 2017 Symposium on Cloud Computing

Pages 242 - 255

https://doi.org/10.1145/3127479.3127485

Published: 24 September 2017 Publication History

Abstract

Cloud computing needs to process and analyze massive high-dimensional data in a real-time manner. Approximate queries in cloud computing systems can provide timely queried results with acceptable accuracy, thus alleviating the consumption of a large amount of resources. Locality Sensitive Hashing (LSH) is able to maintain the data locality and support approximate queries. However, due to randomly choosing hash functions, LSH has to use too many functions to guarantee the query accuracy. The extra computation and storage overheads exacerbate the real performance of LSH. In order to reduce the overheads and deliver high performance, we propose a distribution-aware scheme, called DLSH, to offer cost-effective approximate nearest neighbor query service for cloud computing. The idea of DLSH is to leverage the principal components of the data distribution as the projection vectors of hash functions in LSH, further quantify the weight of each hash function and adjust the interval value in each hash table. We then refine the queried result set based on the hit frequency to significantly decrease the time overhead of distance computation. Extensive experiments in a large-scale cloud computing testbed demonstrate significant improvements in terms of multiple system performance metrics. We have released the source code of DLSH for public use.

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Cited By

Zuo PHua YSun YLiu XWu JGuo YXia WCao SFeng D(2019)Bandwidth and Energy Efficient Image Sharing for Situation Awareness in DisastersIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2018.285993030:1(15-28)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1109/TPDS.2018.2859930
Provatas NKonstantinou IKoziris N(2019)Towards Faster Distributed Deep Learning Using Data Hashing Techniques2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9006378(6189-6191)Online publication date: Dec-2019
https://doi.org/10.1109/BigData47090.2019.9006378

Index Terms

DLSH: a distribution-aware LSH scheme for approximate nearest neighbor query in cloud computing

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cover image ACM Conferences

SoCC '17: Proceedings of the 2017 Symposium on Cloud Computing

September 2017

672 pages

ISBN:9781450350280

DOI:10.1145/3127479

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 24 September 2017

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National Key Research and Development Program of China
State Key Laboratory of Computer Architecture

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SoCC '17

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SoCC '17: ACM Symposium on Cloud Computing

September 24 - 27, 2017

California, Santa Clara

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Overall Acceptance Rate 169 of 722 submissions, 23%

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Cited By

Zuo PHua YSun YLiu XWu JGuo YXia WCao SFeng D(2019)Bandwidth and Energy Efficient Image Sharing for Situation Awareness in DisastersIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2018.285993030:1(15-28)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1109/TPDS.2018.2859930
Provatas NKonstantinou IKoziris N(2019)Towards Faster Distributed Deep Learning Using Data Hashing Techniques2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9006378(6189-6191)Online publication date: Dec-2019
https://doi.org/10.1109/BigData47090.2019.9006378

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