Fault-tolerance in the Borealis distributed stream processing system
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Multi-site cooperative data stream analysis
ACM SIGOPS Operating Systems Review
Policy-based Resource Allocation in Hierarchical Virtual Organizations for Global Grids
SBAC-PAD '06 Proceedings of the 18th International Symposium on Computer Architecture and High Performance Computing
Failure Recovery in Cooperative Data Stream Analysis
ARES '07 Proceedings of the The Second International Conference on Availability, Reliability and Security
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Towards Autonomic Fault Recovery in System-S
ICAC '07 Proceedings of the Fourth International Conference on Autonomic Computing
Dryad: distributed data-parallel programs from sequential building blocks
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
Storage optimization for large-scale distributed stream-processing systems
ACM Transactions on Storage (TOS)
CLASP: collaborating, autonomous stream processing systems
Proceedings of the ACM/IFIP/USENIX 2007 International Conference on Middleware
CLASP: collaborating, autonomous stream processing systems
MIDDLEWARE2007 Proceedings of the 8th ACM/IFIP/USENIX international conference on Middleware
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System S is a large-scale distributed streaming data analysis environment, designed to handle extreme data rates. Multiple System S sites can cooperate to further improve the scale, breadth and depth of data analysis. We describe three autonomic features in the operation of such a cooperative stream processing environment: interoperation models, planning, and failover, They enable the distributed environment to deal with dynamic and rapid changes in imposed workload, available resources, and the priorities of administrators and users. Thus, the system can minimize the human effort needed to operate such large, complex systems.