Multiobjective query optimization
PODS '01 Proceedings of the twentieth 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
Adaptive Control of Extreme-scale Stream Processing Systems
ICDCS '06 Proceedings of the 26th IEEE International Conference on Distributed Computing Systems
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
Multi-objective query processing for database systems
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
SPADE: the system s declarative stream processing engine
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Placement Strategies for Internet-Scale Data Stream Systems
IEEE Internet Computing
Biologically-inspired distributed middleware management for stream processing systems
Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware
Providing QoS Guarantees in Large-Scale Operator Networks
HPCC '10 Proceedings of the 2010 IEEE 12th International Conference on High Performance Computing and Communications
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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Nowadays, many applications processes stream-based data, such as financial market analysis, network intrusion detection, or visualization applications. To process stream-based data in an application-independent manner, distributed stream processing systems emerged. They typically translate a query to an operator graph, place the operators to stream processing nodes, and execute them to process the streamed data. The operator placement is crucial in such systems, as it deeply influences query execution. Often, different stream-based applications require dedicated placement of query graphs according to their specific objectives, e.g. bandwidth not less than 500 MBit/s and costs not more that 1 cost unit. This fact constraints operator placement. Existing approaches do not take into account application-specific objectives, thus not reflecting application-specific placement decisions. As objectives might conflict among each other, operator placement is subject to delicate trade-offs, such as bandwidth maximization is more important than cost reduction. Thus, the challenge is to find a solution which considers the application-specific objectives and their trade-offs. We present M-TOP, an QoS-aware multi-target operator placement framework for data stream systems. Particularly, we propose an operator placement strategy considering application-specific targets consisting of objectives, their respective trade-offs specifications, bottleneck conditions, and ranking schemes to compute a suitable placement. We integrated M-TOP into NexusDS, our distributed data stream processing middleware, and provide an experimental evaluation to show the effectiveness of M-TOP.