Highly available, fault-tolerant, parallel dataflows
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
High-Availability Algorithms for Distributed Stream Processing
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Fault-tolerance in the Borealis distributed stream processing system
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Fast and Highly-Available Stream Processing over Wide Area Networks
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
M-COPE: a multiple continuous query processing engine
Proceedings of the 18th ACM conference on Information and knowledge management
An empirical study of high availability in stream processing systems
Proceedings of the 10th ACM/IFIP/USENIX International Conference on Middleware
Relational database support for event-based middleware functionality
Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems
Detouring and replication for fast and reliable internet-scale stream processing
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Rollback-recovery without checkpoints in distributed event processing systems
Proceedings of the 7th ACM international conference on Distributed event-based systems
Research issues in outlier detection for data streams
ACM SIGKDD Explorations Newsletter
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Borealis-R is a replication-based system for both fast and highly-available processing of data streams over wide-area networks. In Borealis-R, multiple operator replicas send outputs to downstream replicas, allowing each replica to use whichever data arrives first. To further reduce latency, replicas run without coordination, possibly processing data in different orders. Despite this flexibility, Borealis-R guarantees that applications always receive the same results as in the non-replicated, failure-free case. In addition, Borealis-R deploys replicas at select network locations to effectively improve performance as well as availability. We demonstrate the strengths of Borealis-R using a live wide-area monitoring application. We show that Borealis-R outperforms previous solutions in terms of latency and that it uses system resources efficiently by carefully deploying and discarding replicas.