16 January 2006 Streamline: a scheduling heuristic for streaming applications on the grid
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Abstract
Scheduling a streaming application on high-performance computing (HPC) resources has to be sensitive to the computation and communication needs of each stage of the application dataflow graph to ensure QoS criteria such as latency and throughput. Since the grid has evolved out of traditional high-performance computing, the tools available for scheduling are more appropriate for batch-oriented applications. Our scheduler, called Streamline, considers the dynamic nature of the grid and runs periodically to adapt scheduling decisions using application requirements (per-stage computation and communication needs), application constraints (such as co-location of stages), and resource availability. The performance of Streamline is compared with an Optimal placement, Simulated Annealing (SA) approximations, and E-Condor, a streaming grid scheduler built using Condor. For kernels of streaming applications, we show that Streamline performs close to the Optimal and SA algorithms, and an order of magnitude better than E-Condor under non-uniform load conditions. We also conduct scalability studies showing the advantage of Streamline over other approaches.
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Bikash Agarwalla, Nova Ahmed, David Hilley, Umakishore Ramachandran, "Streamline: a scheduling heuristic for streaming applications on the grid", Proc. SPIE 6071, Multimedia Computing and Networking 2006, 607107 (16 January 2006); doi: 10.1117/12.642809; https://doi.org/10.1117/12.642809
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