Aurora: a new model and architecture for data stream management
The VLDB Journal — The International Journal on Very Large Data Bases
Dynamic Load Management for Distributed Continuous Query Systems
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Dynamic Load Distribution in the Borealis Stream Processor
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Operator placement for in-network stream query processing
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Network-Aware Operator Placement for Stream-Processing Systems
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Providing resiliency to load variations in distributed stream processing
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Network-aware query processing for stream-based applications
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Load distribution for distributed stream processing
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
Efficient dynamic operator placement in a locally distributed continuous query system
ODBASE'06/OTM'06 Proceedings of the 2006 Confederated international conference on On the Move to Meaningful Internet Systems: CoopIS, DOA, GADA, and ODBASE - Volume Part I
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A large class of applications require real-time processing of continuous stream data resulting in the development of data stream management systems (DSMS). Since many of these applications are distributed, distributed DSMSs are starting to receive attention. In this paper, we focus on an important issue in distributed DSMS operation, namely load distribution to minimize end-to-end latency. We identify the often conflicting requirements of load distribution, and propose a "potential-driven" load distribution approach to mimic the movements of objects in the physical world. Our approach also takes into account heterogeneous machines, different network conditions, and resource constraints. We present experimental results that investigate our algorithms from various aspects, and show that they outperform existing techniques in terms of end-to-end latency.