By Topic

A Distributed Stream Query Optimization Framework through Integrated Planning and Deployment

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Seshadri, S. ; Coll. of Comput., Georgia Inst. of Technol., Atlanta, GA, USA ; Kumar, Vibhore ; Cooper, B. ; Ling Liu

This paper addresses the problem of optimizing multiple distributed stream queries that are executing simultaneously in distributed data stream systems. We argue that the static query optimization approach of "plan, then deployment" is inadequate for handling distributed queries involving multiple streams and node dynamics faced in distributed data stream systems and applications. Thus, the selection of an optimal execution plan in such dynamic and networked computing systems must consider operator ordering, reuse, network placement, and search space reduction. We propose to use hierarchical network partitions to exploit various opportunities for operator-level reuse while utilizing network characteristics to maintain a manageable search space during query planning and deployment. We develop top-down, bottom-up, and hybrid algorithms for exploiting operator-level reuse through hierarchical network partitions. Formal analysis is presented to establish the bounds on the search space and suboptimality of our algorithms. We have implemented our algorithms in the IFLOW system, an adaptive distributed stream management system. Through simulations and experiments using a prototype deployed on Emulab, we demonstrate the effectiveness of our framework and our algorithms.

Published in:

Parallel and Distributed Systems, IEEE Transactions on  (Volume:20 ,  Issue: 10 )