D-CAPE: distributed and self-tuned continuous query processing
Proceedings of the 14th ACM international conference on Information and knowledge management
Efficient scheduling of heterogeneous continuous queries
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Algorithms and metrics for processing multiple heterogeneous continuous queries
ACM Transactions on Database Systems (TODS)
Tuning QoD in stream processing engines
ADC '10 Proceedings of the Twenty-First Australasian Conference on Database Technologies - Volume 104
Run-time adaptivity for search computing
Search computing
Optimizing adaptive multi-route query processing via time-partitioned indices
Journal of Computer and System Sciences
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Adaptive operator scheduling algorithms for continuous query processing are usually designed to serve a single performance objective, such as minimizing memory usage or maximizing query throughput. We observe that different performance objectives may sometimes conflict with each other. Also due to the dynamic nature of streaming environments, the performance objective may need to change dynamically. Furthermore, the performance specification defined by users may itself be multi-dimensional. Therefore, utilizing a single scheduling algorithm optimized for a single objective is no longer sufficient. In this paper, we propose a novel adaptive scheduling algorithm selection framework named AMoS. It is able to leverage the strengths of existing scheduling algorithms to meet multiple performance objectives. AMoS employs a lightweight learning mechanism to assess the effectiveness of each algorithm. The learned knowledge can be used to select the algorithm that probabilistically has the best chance of improving the performance. In addition, AMoS has the flexibility to add and adapt to new scheduling algorithms, query plans and data sets during execution. Our experimental results show that AMoS significantly outperforms the existing scheduling algorithms with regard to satisfying both uni-objective and multi-objective performance requirements.