Synchronized Disk Interleaving
IEEE Transactions on Computers
Server-directed collective I/O in Panda
Supercomputing '95 Proceedings of the 1995 ACM/IEEE conference on Supercomputing
Disk-directed I/O for MIMD multiprocessors
ACM Transactions on Computer Systems (TOCS)
On implementing MPI-IO portably and with high performance
Proceedings of the sixth workshop on I/O in parallel and distributed systems
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Profile-guided I/O partitioning
ICS '03 Proceedings of the 17th annual international conference on Supercomputing
Noncontiguous I/O Accesses Through MPI-IO
CCGRID '03 Proceedings of the 3st International Symposium on Cluster Computing and the Grid
Data Sieving and Collective I/O in ROMIO
FRONTIERS '99 Proceedings of the The 7th Symposium on the Frontiers of Massively Parallel Computation
GPFS: A Shared-Disk File System for Large Computing Clusters
FAST '02 Proceedings of the 1st USENIX Conference on File and Storage Technologies
Proceedings of the twentieth ACM symposium on Operating systems principles
The automatic improvement of locality in storage systems
ACM Transactions on Computer Systems (TOCS)
Argon: performance insulation for shared storage servers
FAST '07 Proceedings of the 5th USENIX conference on File and Storage Technologies
PVFS: a parallel file system for linux clusters
ALS'00 Proceedings of the 4th annual Linux Showcase & Conference - Volume 4
Improving I/O performance of applications through compiler-directed code restructuring
FAST'08 Proceedings of the 6th USENIX Conference on File and Storage Technologies
BORG: block-reORGanization for self-optimizing storage systems
FAST '09 Proccedings of the 7th conference on File and storage technologies
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Co-scheduling of Disk Head Time in Cluster-Based Storage
SRDS '09 Proceedings of the 2009 28th IEEE International Symposium on Reliable Distributed Systems
InterferenceRemoval: removing interference of disk access for MPI programs through data replication
Proceedings of the 24th ACM International Conference on Supercomputing
I/O deduplication: utilizing content similarity to improve I/O performance
FAST'10 Proceedings of the 8th USENIX conference on File and storage technologies
Evaluating I/O characteristics and methods for storing structured scientific data
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
FAST'11 Proceedings of the 9th USENIX conference on File and stroage technologies
Server-side I/O coordination for parallel file systems
Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
QoS support for end users of I/O-intensive applications using shared storage systems
Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
Pattern-aware file reorganization in MPI-IO
Proceedings of the sixth workshop on Parallel Data Storage
A Prefetching Scheme Exploiting both Data Layout and Access History on Disk
ACM Transactions on Storage (TOS)
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A cluster of data servers and a parallel file system are often used to provide high-throughput I/O service to parallel programs running on a compute cluster. To exploit I/O parallelism parallel file systems stripe file data across the data servers. While this practice is effective in serving asynchronous requests, it may break individual program's spatial locality, which can seriously degrade I/O performance when the data servers concurrently serve synchronous requests from multiple I/O-intensive programs. In this paper we propose a scheme, IOrchestrator, to improve I/O performance of multi-node storage systems by orchestrating I/O services among programs when such inter-data-server coordination is dynamically determined to be cost effective. We have implemented IOrchestrator in the PVFS2 parallel file system. Our experiments with representative parallel benchmarks show that IOrchestrator can significantly improve I/O performance-- by up to a factor of 2.5--delivered by a cluster of data servers servicing concurrently-running parallel programs. Notably, we have not observed any scenarios in which the use of IOrchestrator causes substantial performance degradation.