Operator scheduling in data stream systems

Operator scheduling in data stream systems In many applications involving continuous data streams, data arrival is bursty and data rate fluctuates over time. Systems that seek to give rapid or real-time query responses in such an environment must be prepared to deal gracefully with bursts in data arrival without compromising system performance. We discuss one strategy for processing bursty streams - adaptive, load-aware scheduling of query operators to minimize resource consumption during times of peak load. We show that the choice of an operator scheduling strategy can have significant impact on the runtime system memory usage as well as output latency. Our aim is to design a scheduling strategy that minimizes the maximum runtime system memory while maintaining the output latency within prespecified bounds. We first present Chain scheduling , an operator scheduling strategy for data stream systems that is near-optimal in minimizing runtime memory usage for any collection of single-stream queries involving selections, projections, and foreign-key joins with stored relations. Chain scheduling also performs well for queries with sliding-window joins over multiple streams and multiple queries of the above types. However, during bursts in input streams, when there is a buildup of unprocessed tuples, Chain scheduling may lead to high output latency. We study the online problem of minimizing maximum runtime memory, subject to a constraint on maximum latency. We present preliminary observations, negative results, and heuristics for this problem. A thorough experimental evaluation is provided where we demonstrate the potential benefits of Chain scheduling and its different variants, compare it with competing scheduling strategies, and validate our analytical conclusions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Operator scheduling in data stream systems

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Publisher
Springer Journals
Copyright
Copyright © 2004 by Springer-Verlag
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-004-0132-6
Publisher site
See Article on Publisher Site

Abstract

In many applications involving continuous data streams, data arrival is bursty and data rate fluctuates over time. Systems that seek to give rapid or real-time query responses in such an environment must be prepared to deal gracefully with bursts in data arrival without compromising system performance. We discuss one strategy for processing bursty streams - adaptive, load-aware scheduling of query operators to minimize resource consumption during times of peak load. We show that the choice of an operator scheduling strategy can have significant impact on the runtime system memory usage as well as output latency. Our aim is to design a scheduling strategy that minimizes the maximum runtime system memory while maintaining the output latency within prespecified bounds. We first present Chain scheduling , an operator scheduling strategy for data stream systems that is near-optimal in minimizing runtime memory usage for any collection of single-stream queries involving selections, projections, and foreign-key joins with stored relations. Chain scheduling also performs well for queries with sliding-window joins over multiple streams and multiple queries of the above types. However, during bursts in input streams, when there is a buildup of unprocessed tuples, Chain scheduling may lead to high output latency. We study the online problem of minimizing maximum runtime memory, subject to a constraint on maximum latency. We present preliminary observations, negative results, and heuristics for this problem. A thorough experimental evaluation is provided where we demonstrate the potential benefits of Chain scheduling and its different variants, compare it with competing scheduling strategies, and validate our analytical conclusions.

Journal

The VLDB JournalSpringer Journals

Published: Dec 1, 2004

References

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