Designing file systems for digital video and audio
SOSP '91 Proceedings of the thirteenth ACM symposium on Operating systems principles
Streaming RAID: a disk array management system for video files
MULTIMEDIA '93 Proceedings of the first ACM international conference on Multimedia
Staggered striping in multimedia information systems
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Scheduling policies for an on-demand video server with batching
MULTIMEDIA '94 Proceedings of the second ACM international conference on Multimedia
On multimedia repositories, personal computers, and hierarchical storage systems
MULTIMEDIA '94 Proceedings of the second ACM international conference on Multimedia
Channel allocation under batching and VCR control in video-on-demand systems
Journal of Parallel and Distributed Computing - Special issue on multimedia processing and technology
Reducing I/O demand in video-on-demand storage servers
Proceedings of the 1995 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
On optimal piggyback merging policies for video-on-demand systems
Proceedings of the 1996 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Efficient Storage Techniques for Digital Continuous Multimedia
IEEE Transactions on Knowledge and Data Engineering
A Low-Cost Storage Server for Movie on Demand Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Continuous Media Sharing in Multimedia Database Systems
Proceedings of the 4th International Conference on Database Systems for Advanced Applications (DASFAA)
Buffering and caching in large-scale video servers
COMPCON '95 Proceedings of the 40th IEEE Computer Society International Conference
Buffer Management For Continuous Media Sharing In Multimedia Databse Systems
Buffer Management For Continuous Media Sharing In Multimedia Databse Systems
| Hi-index | 0.00 |
There have been many proposals on how media-on-demand servers can effectively allow clients to share resources. In this paper, given a set of clients, we show how these clients may be partitioned into 驴sync-classes驴驴sets of clients who can be serviced through allocation of a single set of resources. As a set of clients may be partitioned into sync-classes in many different ways, we show that a very large class of cost functions may be used to determine which partition to choose. We provide algorithms to compute such optimal splits. Our framework is very generic in the following ways: 1) The system may plug-in any cost function whatsoever, as long as it satisfies four common-sense axioms that evaluate costs, and 2) the system may evaluate the future anticipated requests of a user using any user model (e.g., a Markovian model) that has a specified I/O interface. Thus, a wide variety of predictive methods (of what the user will do) and a wide variety of costing methods may be used within our framework.