Lightweight monitoring of distributed streams

Arnon Lazerson, Daniel Keren, Assaf Schuster

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

As data becomes dynamic, large, and distributed, there is increasing demand for what have become known as distributed stream algorithms. Since continuously collecting the data to a central server and processing it there incurs very high communication and computation complexities, it is advantageous to deffne local conditions at the nodes, such that - as long as they are maintained - some desirable global condition holds. A generic algorithm which proved very useful for reducing communication in distributed streaming environments is geometric monitoring (GM). Alas, applying GM to many important tasks is computationally very demanding, as it requires solving a notoriously difficult problem - computing the distance between a point and a surface, which is often very time-consuming even in low dimensions. Thus, while useful for reducing communication, GM often suffers from exceedingly heavy computational burden at the nodes, which renders it very problematic to apply, especially for "thin", battery-operated sensors, which are prevalent in numerous applications, including the "Internet of Things" paradigm. Here we propose a very different approach, designated CB (for Convex/Concave Bounds). CB is based on directly bounding the monitored function by suitably chosen convex and concave functions, that naturally enable monitoring distributed streams. These functions can be checked on the fly, yielding far simpler local conditions than those applied by GM. CB's superiority over GM is demonstrated in reducing computational complexity, by several orders of magnitude in some cases. As an added bonus, CB also reduced communi- cation overhead in all application scenarios we tested.

Original languageEnglish
Title of host publicationKDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages1685-1694
Number of pages10
ISBN (Electronic)9781450342322
DOIs
StatePublished - 13 Aug 2016
Event22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016 - San Francisco, United States
Duration: 13 Aug 201617 Aug 2016

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Volume13-17-August-2016

Conference

Conference22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016
Country/TerritoryUnited States
CitySan Francisco
Period13/08/1617/08/16

Bibliographical note

Publisher Copyright:
© 2016 ACM.

Keywords

  • Disributed streams
  • Distributed monitoring
  • Resource limited devices

ASJC Scopus subject areas

  • Software
  • Information Systems

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