Cluster I/O with River: making the fast case common
Proceedings of the sixth workshop on I/O in parallel and distributed systems
Exploiting Punctuation Semantics in Continuous Data Streams
IEEE Transactions on Knowledge and Data Engineering
Flux: An Adaptive Partitioning Operator for Continuous Query Systems
Flux: An Adaptive Partitioning Operator for Continuous Query Systems
Dynamic Load Distribution in the Borealis Stream Processor
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Network-Aware Operator Placement for Stream-Processing Systems
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Providing resiliency to load variations in distributed stream processing
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Integrating scale out and fault tolerance in stream processing using operator state management
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Automatic optimization of stream programs via source program operator graph transformations
Distributed and Parallel Databases
Semantic-based QoS management in cloud systems: Current status and future challenges
Future Generation Computer Systems
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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. We 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. We 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. We demonstrate the effectiveness of our framework with preliminary experimental results using a variety of real-world applications on heterogeneous clusters.