High-Availability Algorithms for Distributed Stream Processing
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Distributed Stream Management using Utility-Driven Self-Adaptive Middleware
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Adaptive Control of Extreme-scale Stream Processing Systems
ICDCS '06 Proceedings of the 26th IEEE International Conference on Distributed Computing Systems
Active Replication at (Almost) No Cost
SRDS '11 Proceedings of the 2011 IEEE 30th International Symposium on Reliable Distributed Systems
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A growing number of applications require continuous processing of high-throughput data streams, e.g., financial analysis, network traffic monitoring, or Big Data analytics in smart cities. Stream processing applications typically have explicit quality-of-service requirements; yet, due to the high time-variability of stream characteristics, it is inefficient and sometimes impossible to statically allocate all the resources needed to guarantee application SLAs. In this work, we present DARM, a novel middleware for adaptive replication that trades fault-tolerance for increased capacity during load spikes and provides guaranteed upper-bounds on information loss in case of failures.