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Bobby Jose

dblp:61/1114 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Storage systems
data placement
0.011999
Resource Scheduling In A High-Performance Multimedia Server · IEEE Trans. Knowl. Data Eng. 1999
Storage systems › i/o scheduling
disk scheduling
0.011999
Resource Scheduling In A High-Performance Multimedia Server · IEEE Trans. Knowl. Data Eng. 1999
Storage systems
multimedia storage
0.011999
Resource Scheduling In A High-Performance Multimedia Server · IEEE Trans. Knowl. Data Eng. 1999
Cloud and datacenter computing › datacenter architecture
server architecture
0.011999
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
YearPublicationVenuePosition
1999 Resource Scheduling In A High-Performance Multimedia Server
abstract
Supporting 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