Multimedia Tools and Applications
Experiences Using Domain Specific Techniques within Multimedia Software Engineering
Annals of Software Engineering
Popularity-Independent Multimedia-on-Demand Server Model
COMPSAC '00 24th International Computer Software and Applications Conference
Variable-size data item placement for load and storage balancing
Journal of Systems and Software
Evolution and challenges in multimedia
IBM Journal of Research and Development - Papers on mustimedia systems
Dynamic placement for clustered web applications
Proceedings of the 15th international conference on World Wide Web
Optimizing I/O server placement for parallel I/O on switch-based irregular networks
The Journal of Supercomputing
Optimizing server placement for parallel I/O in switch-based clusters
Journal of Parallel and Distributed Computing
Journal of Parallel and Distributed Computing
User demand behavior based adaptive algorithm for service composition of streaming media
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Optimizing server placement in distributed systems in the presence of competition
Journal of Parallel and Distributed Computing
Load and storage balanced posting file partitioning for parallel information retrieval
Journal of Systems and Software
Optimizing i/o server placement for parallel i/o on switch-based irregular networks
ISPA'04 Proceedings of the Second international conference on Parallel and Distributed Processing and Applications
Dynamic application placement under service and memory constraints
WEA'05 Proceedings of the 4th international conference on Experimental and Efficient Algorithms
Load splitting in clusters of video servers
Computer Communications
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In distributed multimedia servers where client requests for different video streams may have different probabilities, placement of video streams is an important parameter because it may result in unbalanced requests to the system's stations, and thus to high blocking probabilities of requests. We present a method, MMPacking, to balance traffic load and storage use in a distributed server environment. Since different video streams are requested by clients with different rates, video stream replication is used to balance the traffic patterns of the stations; thus, the requests and I/O usage of the stations are balanced, since replication allows requests for the same video stream to be routed to different stations. MMPacking achieves load balancing by producing at most N-1 replicas of video streams in a system with N servers. These replicas are distributed among the stations so that storage balancing is achieved as well, since no station stores more than two video streams more than any other station in the system