Active Storage for Large-Scale Data Mining and Multimedia
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Distributed Computing with Load-Managed Active Storage
HPDC '02 Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Dryad: distributed data-parallel programs from sequential building blocks
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
A Survey of Cloud Platforms and Their Future
ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part I
An auxiliary storage subsystem to distributed computing systems for external storage service
ICA3PP'10 Proceedings of the 10th international conference on Algorithms and Architectures for Parallel Processing - Volume Part I
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Active storage clouds are an attractive platform for executing large data intensive workloads found in many fields of science. However, active storage presents new system management challenges. A large system of fault-prone machines with local persistent state can easily degenerate into a mess of unreferenced data and runaway computations. Our solution to this problem is DataLab, a software framework for running data parallel workloads on active storage clusters. DataLab provides a simple language for expressing workloads, works with legacy application codes, and achieves robustness through the use of distributed transactions. Our prototype implementation scales to 250 nodes on a large biometric image processing workload.