Parallel database systems: the future of high performance database systems
Communications of the ACM
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Scalable, Adaptive Load Sharing for Distributed Systems
IEEE Parallel & Distributed Technology: Systems & Technology
Strategies for Dynamic Load Balancing on Highly Parallel Computers
IEEE Transactions on Parallel and Distributed Systems
Dynamic Multi-Resource Load Balancing in Parallel Database Systems
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Dynamic Load Balancing in Hierarchical Parallel Database Systems
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Monitoring streams: a new class of data management applications
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Operator scheduling in a data stream manager
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Potential-driven load distribution for distributed data stream processing
SSPS '08 Proceedings of the 2nd international workshop on Scalable stream processing system
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Distributed steam processing is necessary for a large class of stream-based applications To exploit the full power of distributed computation, effective load distribution techniques must be developed to optimize the system performance and cope with time-varying loads When traditional load balancing or load sharing strategies are applied to such systems, we find that they either fall short in achieving good load distribution or fail to maintain good task partition in the long run. In this paper, we study two important issues of dynamic load distribution in the context of data-intensive stream processing The first one is how to allocate processing resources for push-based tasks such that the average end-to-end data processing latency can be minimized The second issue is how to maintain a good load distribution dynamically for long running continuous queries We propose a new hybrid load distribution strategy that addresses the above concerns by load clustering To achieve scalability, our algorithm is completely decentralized and asynchronous.