Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Hippodrome: Running Circles Around Storage Administration
FAST '02 Proceedings of the Conference on File and Storage Technologies
Managable Storage via Adaptation in WiND
CCGRID '01 Proceedings of the 1st International Symposium on Cluster Computing and the Grid
Using tertiary storage in video-on-demand servers
COMPCON '95 Proceedings of the 40th IEEE Computer Society International Conference
Two decades of policy-based storage management for the IBM mainframe computer
IBM Systems Journal
An Architecture for Lifecycle Management in Very Large File Systems
MSST '05 Proceedings of the 22nd IEEE / 13th NASA Goddard Conference on Mass Storage Systems and Technologies
Position: short object lifetimes require a delete-optimized storage system
Proceedings of the 11th workshop on ACM SIGOPS European workshop
CHAMELEON: a self-evolving, fully-adaptive resource arbitrator for storage systems
ATEC '05 Proceedings of the annual conference on USENIX Annual Technical Conference
Multi-site cooperative data stream analysis
ACM SIGOPS Operating Systems Review
Storage optimization for large-scale distributed stream-processing systems
ACM Transactions on Storage (TOS)
| Hi-index | 0.00 |
The efficiency of large-scale applications is strongly dependent on good data management techniques. In this paper, we claim that the ability to specify data requirements in a time-varying manner facilitates data management and improves application efficiency. This is because requirements such as availability, bandwidth and latency can vary significantly with time. Consequently, the storage system can dynamically change the allocation of resources to data objects. We describe how an application may specify these dynamic requirements using utility functions, and outline a strategy towards achieving an optimal allocation of resources to data objects.