NFS illustrated
A scalable cross-platform infrastructure for application performance tuning using hardware counters
Proceedings of the 2000 ACM/IEEE conference on Supercomputing
Minerva: An automated resource provisioning tool for large-scale storage systems
ACM Transactions on Computer Systems (TOCS)
Predictive performance and scalability modeling of a large-scale application
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
Data Management: NetCDF: an Interface for Scientific Data Access
IEEE Computer Graphics and Applications
Characterizing parallel file-access patterns on a large-scale multiprocessor
IPPS '95 Proceedings of the 9th International Symposium on Parallel Processing
Prophesy: an infrastructure for performance analysis and modeling of parallel and grid applications
ACM SIGMETRICS Performance Evaluation Review
Data Sieving and Collective I/O in ROMIO
FRONTIERS '99 Proceedings of the The 7th Symposium on the Frontiers of Massively Parallel Computation
A Statistically Rigorous Approach for Improving Simulation Methodology
HPCA '03 Proceedings of the 9th International Symposium on High-Performance Computer Architecture
Improving MPI-IO Output Performance with Active Buffering Plus Threads
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
Cross-architecture performance predictions for scientific applications using parameterized models
Proceedings of the joint international conference on Measurement and modeling of computer systems
Cross-Platform Performance Prediction of Parallel Applications Using Partial Execution
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Modeling the relative fitness of storage
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Optimizing system configurations quickly by guessing at the performance
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
PVFS: a parallel file system for linux clusters
ALS'00 Proceedings of the 4th annual Linux Showcase & Conference - Volume 4
Towards an I/O tracing framework taxonomy
PDSW '07 Proceedings of the 2nd international workshop on Petascale data storage: held in conjunction with Supercomputing '07
CprFS: a user-level file system to support consistent file states for checkpoint and restart
Proceedings of the 22nd annual international conference on Supercomputing
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Parallel I/O prefetching using MPI file caching and I/O signatures
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Binary analysis for measurement and attribution of program performance
Proceedings of the 2009 ACM SIGPLAN conference on Programming language design and implementation
I/O performance challenges at leadership scale
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
Proceedings of the 15th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
Communications of the ACM
Performance modeling in industry: a case study on storage virtualization
Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 2
Practical performance models for complex, popular applications
Proceedings of the ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Data Sharing Options for Scientific Workflows on Amazon EC2
Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis
The Hadoop Distributed File System
MSST '10 Proceedings of the 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST)
Coordinating Computation and I/O in Massively Parallel Sequence Search
IEEE Transactions on Parallel and Distributed Systems
Hippodrome: running circles around storage administration
FAST'02 Proceedings of the 1st USENIX conference on File and storage technologies
Proceedings of the 2nd ACM Symposium on Cloud Computing
No one (cluster) size fits all: automatic cluster sizing for data-intensive analytics
Proceedings of the 2nd ACM Symposium on Cloud Computing
Pesto: online storage performance management in virtualized datacenters
Proceedings of the 2nd ACM Symposium on Cloud Computing
State of the Practice Reports
One optimized I/O configuration per HPC application: leveraging the configurability of cloud
Proceedings of the Second Asia-Pacific Workshop on Systems
A performance analysis framework for identifying potential benefits in GPGPU applications
Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming
scc: cluster storage provisioning informed by application characteristics and SLAs
FAST'12 Proceedings of the 10th USENIX conference on File and Storage Technologies
Scalia: an adaptive scheme for efficient multi-cloud storage
SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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The cloud has become a promising alternative to traditional HPC centers or in-house clusters. This new environment highlights the I/O bottleneck problem, typically with top-of-the-line compute instances but sub-par communication and I/O facilities. It has been observed that changing cloud I/O system configurations leads to significant variation in the performance and cost efficiency of I/O intensive HPC applications. However, storage system configuration is tedious and error-prone to do manually, even for experts. This paper proposes ACIC, which takes a given application running on a given cloud platform, and automatically searches for optimized I/O system configurations. ACIC utilizes machine learning models to perform black-box performance/cost predictions. To tackle the high-dimensional parameter exploration space unique to cloud platforms, we enable affordable, reusable, and incremental training guided by Plackett and Burman Matrices. Results with four representative applications indicate that ACIC consistently identifies near-optimal configurations among a large group of candidate settings.