PVFS: A Parallel Virtual File System for Linux Clusters
Linux Journal
Using moldability to improve the performance of supercomputer jobs
Journal of Parallel and Distributed Computing
Hippodrome: Running Circles Around Storage Administration
FAST '02 Proceedings of the Conference on File and Storage Technologies
Quickly finding near-optimal storage designs
ACM Transactions on Computer Systems (TOCS)
Ursa minor: versatile cluster-based storage
FAST'05 Proceedings of the 4th conference on USENIX Conference on File and Storage Technologies - Volume 4
Using utility to provision storage systems
FAST'08 Proceedings of the 6th USENIX Conference on File and Storage Technologies
Informed data distribution selection in a self-predicting storage system
ICAC '06 Proceedings of the 2006 IEEE International Conference on Autonomic Computing
Case studies in storage access by loosely coupled petascale applications
Proceedings of the 4th Annual Workshop on Petascale Data Storage
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Assessing data deduplication trade-offs from an energy and performance perspective
IGCC '11 Proceedings of the 2011 International Green Computing Conference and Workshops
Swift: A language for distributed parallel scripting
Parallel Computing
A Workflow-Aware Storage System: An Opportunity Study
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
Design and analysis of data management in scalable parallel scripting
SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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System configuration decisions for I/O-intensive workflow applications can be complex even for expert users. Users face decisions to configure several parameters optimally (e.g., replication level, chunk size, number of storage node) - each having an impact on overall application performance. This paper presents our progress on addressing the problem of supporting storage system configuration decisions for workflow applications. Our approach accelerates the exploration of the configuration space based on a low-cost performance predictor that estimates turn-around time of a workflow application in a given setup. Our evaluation shows that the predictor is effective in identifying the desired system configuration, and it is lightweight using 2000-5000× less resources (machines × time) than running the actual benchmarks.