Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Flexible Information Discovery in Decentralized Distributed Systems
HPDC '03 Proceedings of the 12th IEEE International Symposium on High Performance Distributed Computing
An Automated Profiling Subsystem for QoS-Aware Services
RTAS '00 Proceedings of the Sixth IEEE Real Time Technology and Applications Symposium (RTAS 2000)
BRITE: An Approach to Universal Topology Generation
MASCOTS '01 Proceedings of the Ninth International Symposium in Modeling, Analysis and Simulation of Computer and Telecommunication Systems
Vivaldi: a decentralized network coordinate system
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
Optimal Component Composition for Scalable Stream Processing
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
Network-Aware Operator Placement for Stream-Processing Systems
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Biologically-inspired distributed middleware management for stream processing systems
Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware
QoS-Aware Shared Component Composition for Distributed Stream Processing Systems
IEEE Transactions on Parallel and Distributed Systems
Adaptive and decentralized operator placement for in-network query processing
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
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Distributed stream processing relies on in-network operator placement to achieve an optimal resource allocation which can use the pool of machines and network resource efficiently. Due to the QoS (Quality of Service) constraints imposed by the application, operator placement is usually treated as an optimization problem with constraints. Trying to get a global optimization is challenging since it's a NP-hard problem. In this paper, we formalize the operator placement problem with network usage as the optimization objective and use two resource allocation related QoS metrics: throughput and end-to-end delay. We propose a concept of Optimization Power to describe the host's capacity to reach a global optimal solution as soon as possible. We also propose a corresponding Optimization Power-based heuristic algorithm for operator placement. Experiment results show that our approach can achieve a better performance in terms of reducing network usage and end-top-end delay, improving success ratio, and decreasing resource discovery frequency, compared to some other placement algorithms.