EDBT 2026 Demo / reviewers in the wild / expert
Bobby Jose
dblp:61/1114
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1999
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Storage systems · 91% Cloud and datacenter computing · 9% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Storage systems
data placement |
0.0 | 1 | 1999 | Resource Scheduling In A High-Performance Multimedia Server · IEEE Trans. Knowl. Data Eng. 1999 |
Storage systems › i/o scheduling
disk scheduling |
0.0 | 1 | 1999 | Resource Scheduling In A High-Performance Multimedia Server · IEEE Trans. Knowl. Data Eng. 1999 |
Storage systems
multimedia storage |
0.0 | 1 | 1999 | Resource Scheduling In A High-Performance Multimedia Server · IEEE Trans. Knowl. Data Eng. 1999 |
Cloud and datacenter computing › datacenter architecture
server architecture |
0.0 | 1 | 1999 | Resource Scheduling In A High-Performance Multimedia Server · IEEE Trans. Knowl. Data Eng. 1999 |
Methods — techniques the papers use, named apart from their topics
replication · 0.0load balancing · 0.0declustering · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1999 | Resource Scheduling In A High-Performance Multimedia ServerabstractSupporting continuous media data-such as video and audio-imposes stringent demands on the retrieval performance of a multimedia server. In this paper, we propose and evaluate a set of data placement and retrieval algorithms to exploit the full capacity of the disks in a multimedia server. The data placement algorithm declusters every object over all of the disks in the server-using a time-based declustering unit-with the aim of balancing the disk load. As for runtime retrieval, the quintessence of the algorithm is to give each disk advance notification of the blocks that have to be fetched in the impending time periods, so that the disk can optimize its service schedule accordingly. Moreover, in processing a block request for a replicated object, the server will dynamically channel the retrieval operation to the most lightly loaded disk that holds a copy of the required block. We have implemented a multimedia server based on these algorithms. Performance tests reveal that the server achieves very high disk efficiency. Specifically, each disk is able to support up to 25 MPEG-1 streams. Moreover, experiments suggest that the aggregate retrieval capacity of the server scales almost linearly with the number of disks. HweeHwa Pang, Bobby Jose, Mayuram S. Krishnan |
IEEE Trans. Knowl. Data Eng. | 2 |