Elastic scaling for data stream processing
Wu, K L.
IEEE Transactions on Parallel and Distributed Systems
Gedik, B., Schneider, S., Hirzel, M., & Wu, K. L. (2014). Elastic scaling for data stream processing. Parallel and Distributed Systems, IEEE Transactions on, 25(6), 1447-1463.
Please cite this item using this persistent URLhttp://hdl.handle.net/11693/12708
This article 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 article, 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.