Lightweight monitoring of distributed streams

Arnon Lazerson, Daniel Keren, Assaf Schuster

פרסום מחקרי: פרק בספר / בדוח / בכנספרסום בספר כנסביקורת עמיתים

תקציר

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.

שפה מקוריתאנגלית אמריקאית
כותר פרסום המארחKDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
עמודים1685-1694
מספר עמודים10
מסת"ב (אלקטרוני)9781450342322
מזהי עצם דיגיטלי (DOIs)
סטטוס פרסוםפורסם - 13 אוג׳ 2016
אירוע22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016 - San Francisco, ארצות הברית
משך הזמן: 13 אוג׳ 201617 אוג׳ 2016

סדרות פרסומים

שםProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
כרך13-17-August-2016

כנס

כנס22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016
מדינה/אזורארצות הברית
עירSan Francisco
תקופה13/08/1617/08/16

ASJC Scopus subject areas

  • ???subjectarea.asjc.1700.1712???
  • ???subjectarea.asjc.1700.1710???

טביעת אצבע

להלן מוצגים תחומי המחקר של הפרסום 'Lightweight monitoring of distributed streams'. יחד הם יוצרים טביעת אצבע ייחודית.

פורמט ציטוט ביבליוגרפי