On the competitiveness of on-line real-time task scheduling
Real-Time Systems
On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
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IEEE/ACM Transactions on Networking (TON)
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Journal of the ACM (JACM)
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SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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IEEE Transactions on Knowledge and Data Engineering
Chain: operator scheduling for memory minimization in data stream systems
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Data Mining Meets Performance Evaluation: Fast Algorithms for Modeling Bursty Traffic
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Best-effort decision-making for real-time scheduling
Best-effort decision-making for real-time scheduling
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PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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Proceedings of the 14th ACM international conference on Information and knowledge management
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VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Load shedding in a data stream manager
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Operator scheduling in a data stream manager
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
QoS-Oriented Multi-query Scheduling over Data Streams
DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
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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.