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Stream processing applications run continuously and have varying load. Cloud infrastructures present an attractive option to meet these fluctuating computational demands. Coordinating such resources to meet end-to-end latency objectives efficiently is important in preventing the frivolous use of cloud resources. The authors present a framework that parallelizes and schedules workflows of stream operators, in real-time, to meet latency objectives. It supports data- and task-parallel processing of all workflow operators, by all computing nodes, while maintaining the ordering properties of sorted data streams. They show that a latency-oriented operator scheduling policy coupled with the diversification of computing node responsibilities encourages parallelism models that achieve end-to-end latency-minimization goals. They demonstrate the effectiveness of their framework with preliminary experimental results using a variety of real-world applications on heterogeneous clusters.
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