Elastic Scaling for Data Stream Processing

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This paper addresses the profitability problem associated with auto-parallelization of general-purpose distributed data stream processing applications. Auto-parallelization involves locating regions in the application’s data flow graph that can be replicated at run-time to apply data partitioning, in order to achieve scale. In order to make auto-parallelization effective in practice, the profitability question needs to be answered: How many parallel channels provide the best throughput? The answer to this question changes depending on the workload dynamics and resource availability at run-time. In this paper, we propose an elastic auto-parallelization solution that can dynamically adjust the number of channels used to achieve high throughput without unnecessarily wasting resources. Most importantly, our solution can handle partitioned stateful operators via run-time state migration, which is fully transparent to the application developers. We provide an implementation and evaluation of the system on an industrial-strength data stream processing platform to validate our solution.

By: Bugra Gedik, Scott Schneider, Martin Hirzel, Kun-Lung Wu

Published in: IEEE Transactions on Parallel and Distributed Systems, volume 25, (no 6), pages 1447-63 in 2014


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