Multi-Query Scheduling for Time-Critical Data Stream Applications

Yongluan Zhou, Ji Wu, Ahmed Khan Leghari

Research output: Contribution to journalConference articleResearchpeer-review

Abstract

Many data stream applications, such as network intrusion detection, on-line financial tickers and environmental monitoring, typically exhibit certain "real-time" traits. In such applications, people are interested in strategies that ensure on-time delivery of query results. In this paper, we point out that traditional operator-based query scheduling strategies are insufficient to handle this class of problem. Therefore we choose to approach the issue from a new angle by modeling multi-query scheduling as a job-scheduling problem, a classical problem in real-time computing. By taking advantage of the wisdom in the real-time computing community, we propose several new scheduling strategies and algorithms to enhance the overall data stream query scheduling performance. Through extensive experiments over both real and synthetic data, we identify the important factors for scheduling performance and verify the effectiveness of our approaches.

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Scheduling
Intrusion detection
Monitoring
Experiments

Cite this

@inproceedings{3968d228ba2a4799b74beb1a7514b88a,
title = "Multi-Query Scheduling for Time-Critical Data Stream Applications",
abstract = "Many data stream applications, such as network intrusion detection, on-line financial tickers and environmental monitoring, typically exhibit certain {"}real-time{"} traits. In such applications, people are interested in strategies that ensure on-time delivery of query results. In this paper, we point out that traditional operator-based query scheduling strategies are insufficient to handle this class of problem. Therefore we choose to approach the issue from a new angle by modeling multi-query scheduling as a job-scheduling problem, a classical problem in real-time computing. By taking advantage of the wisdom in the real-time computing community, we propose several new scheduling strategies and algorithms to enhance the overall data stream query scheduling performance. Through extensive experiments over both real and synthetic data, we identify the important factors for scheduling performance and verify the effectiveness of our approaches.",
author = "Yongluan Zhou and Ji Wu and Leghari, {Ahmed Khan}",
year = "2013",
month = "7",
doi = "10.1145/2484838.2484864",
language = "English",
journal = "Proceedings of the 25th International Conference on Scientific and Statistical Database Management",
publisher = "Association for Computing Machinery",

}

Multi-Query Scheduling for Time-Critical Data Stream Applications. / Zhou, Yongluan; Wu, Ji; Leghari, Ahmed Khan.

In: Proceedings of the 25th International Conference on Scientific and Statistical Database Management, 07.2013.

Research output: Contribution to journalConference articleResearchpeer-review

TY - GEN

T1 - Multi-Query Scheduling for Time-Critical Data Stream Applications

AU - Zhou, Yongluan

AU - Wu, Ji

AU - Leghari, Ahmed Khan

PY - 2013/7

Y1 - 2013/7

N2 - Many data stream applications, such as network intrusion detection, on-line financial tickers and environmental monitoring, typically exhibit certain "real-time" traits. In such applications, people are interested in strategies that ensure on-time delivery of query results. In this paper, we point out that traditional operator-based query scheduling strategies are insufficient to handle this class of problem. Therefore we choose to approach the issue from a new angle by modeling multi-query scheduling as a job-scheduling problem, a classical problem in real-time computing. By taking advantage of the wisdom in the real-time computing community, we propose several new scheduling strategies and algorithms to enhance the overall data stream query scheduling performance. Through extensive experiments over both real and synthetic data, we identify the important factors for scheduling performance and verify the effectiveness of our approaches.

AB - Many data stream applications, such as network intrusion detection, on-line financial tickers and environmental monitoring, typically exhibit certain "real-time" traits. In such applications, people are interested in strategies that ensure on-time delivery of query results. In this paper, we point out that traditional operator-based query scheduling strategies are insufficient to handle this class of problem. Therefore we choose to approach the issue from a new angle by modeling multi-query scheduling as a job-scheduling problem, a classical problem in real-time computing. By taking advantage of the wisdom in the real-time computing community, we propose several new scheduling strategies and algorithms to enhance the overall data stream query scheduling performance. Through extensive experiments over both real and synthetic data, we identify the important factors for scheduling performance and verify the effectiveness of our approaches.

U2 - 10.1145/2484838.2484864

DO - 10.1145/2484838.2484864

M3 - Conference article

JO - Proceedings of the 25th International Conference on Scientific and Statistical Database Management

JF - Proceedings of the 25th International Conference on Scientific and Statistical Database Management

ER -