Multi-query scheduling for time-critical data stream applications

TY - GEN

T1 - Multi-query scheduling for time-critical data stream applications

AU - Zhou, Yongluan

AU - Wu, Ji

AU - Leghari, Ahmed Khan

N1 - Conference code: 25

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 - Article in proceedings

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

PB - Association for Computing Machinery

T2 - 25th International Conference on Scientific and Statistical Database Management

Y2 - 29 July 2013 through 31 July 2013

ER -