ACM Transactions on Computer Systems (TOCS)
Beating the I/O bottleneck: a case for log-structured file systems
ACM SIGOPS Operating Systems Review
Dynamic file migration in distributed computer systems
Communications of the ACM
The design and implementation of a log-structured file system
ACM Transactions on Computer Systems (TOCS)
Informed prefetching and caching
SOSP '95 Proceedings of the fifteenth ACM symposium on Operating systems principles
The HP AutoRAID hierarchical storage system
SOSP '95 Proceedings of the fifteenth ACM symposium on Operating systems principles
Deciding when to forget in the Elephant file system
Proceedings of the seventeenth ACM symposium on Operating systems principles
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Efficient Bulk Deletes in Relational Databases
Proceedings of the 17th International Conference on Data Engineering
WOLF--A Novel Reordering Write Buffer to Boost the Performance of Log-Structured File System
FAST '02 Proceedings of the 1st USENIX Conference on File and Storage Technologies
Multi-site cooperative data stream analysis
ACM SIGOPS Operating Systems Review
Diamonds are forever, files are not
FAST '07 Proceedings of the 5th USENIX conference on File and Storage Technologies
Storage optimization for large-scale distributed stream-processing systems
ACM Transactions on Storage (TOS)
SODA: an optimizing scheduler for large-scale stream-based distributed computer systems
Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware
COLA: optimizing stream processing applications via graph partitioning
Proceedings of the 10th ACM/IFIP/USENIX International Conference on Middleware
COLA: optimizing stream processing applications via graph partitioning
Middleware'09 Proceedings of the ACM/IFIP/USENIX 10th international conference on Middleware
Time-varying management of data storage
HotDep'05 Proceedings of the First conference on Hot topics in system dependability
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Early file systems were designed with the expectation that data would typically be read from disk many times before being deleted; on-disk structures were therefore optimized for reading. As main memory sizes increased, more read requests could be satisfied from data cached in memory, motivating file system designs that optimize write performance. Here, we describe how one might build a storage system that optimizes not only reading and writing, but creation and deletion as well. Efficiency is achieved, in part, by automating deletion based on relative retention values rather than requiring data be deleted explicitly by an application. This approach is well suited to an emerging class of applications that process data at consistently high rates of ingest. This paper explores trade-offs in clustering data by retention value and age and examines the effects of allowing the retention values to change under application control.