On the execution of parallel programs on multiprocessor systems—a queuing theory approach
Journal of the ACM (JACM)
Fair end-to-end window-based congestion control
IEEE/ACM Transactions on Networking (TON)
GridFlow: Workflow Management for Grid Computing
CCGRID '03 Proceedings of the 3st International Symposium on Cluster Computing and the Grid
Adaptive Control of Extreme-scale Stream Processing Systems
ICDCS '06 Proceedings of the 26th IEEE International Conference on Distributed Computing Systems
Load shedding and distributed resource control of stream processing networks
Performance Evaluation
Load shedding in a data stream manager
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
IEEE Transactions on Information Theory
Proceedings of the ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Linear-speed interior-path algorithms for distributed control of information networks
Performance Evaluation
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Many emerging information processing applications require applying various fork and join type operations such as correlation, aggregation, and encoding/decoding to data streams in real-time. Each operation will require one or more simultaneous input data streams and produce one or more output streams, where the processing may shrink or expand the data rates upon completion. Multiple tasks can be co-located on the same server and compete for limited resources. Effective in-network processing and resource management in a distributed heterogeneous environment is critical to achieving better scalability and provision of quality of service. In this paper, we study the distributed resource allocation problem for a synchronous fork and join processing network, with the goal of achieving the maximum total utility of output streams. Using primal and dual based optimization techniques, we propose several decentralized iterative algorithms to solve the problem, and design protocols that implement these algorithms. These algorithms have different strengths in practical implementation and can be tailored to take full advantage of the computing capabilities of individual servers. We show that our algorithms guarantee optimality and demonstrate through simulation that they can adapt quickly to dynamically changing environments.