Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Communications of the ACM
An efficient bandwidth-sharing technique for true video on demand systems
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary Algorithms in Engineering and Computer Science: Recent Advances in Genetic Algorithms, Evolution Strategies, Evolutionary Programming, GE
Genetic Algorithms
Introduction to Probability and Statistics: Principles and Applications for Engineering and the Computing Sciences
File and Object Replication in Data Grids
Cluster Computing
Access Time Minimization for Distributed Multimedia Applications
Multimedia Tools and Applications
Parallel Video Servers: A Tutorial
IEEE MultiMedia
Multimedia Broadcasting over the Internet: Part I
IEEE MultiMedia
Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Efficient Movie Retrieval Strategies for Movie-on-Demand Multimedia Services on Distributed Networks
Multimedia Tools and Applications
Gap-Based Modeling of Packet Losses over the Internet
MASCOTS '02 Proceedings of the 10th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems
Optimized Distributed Delivery of Continuous-Media Documents over Unreliable Communication Links
IEEE Transactions on Parallel and Distributed Systems
Distributed Multimedia Retrieval Strategies for Large Scale Networked Systems (Multimedia Systems and Applications)
Journal of Parallel and Distributed Computing
Multimedia Tools and Applications
Multispectral and multiresolution image fusion using particle swarm optimization
Multimedia Tools and Applications
Search-based software engineering: Trends, techniques and applications
ACM Computing Surveys (CSUR)
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In this paper we present the design and explore the performance of a unicast-based distributed system for Movie-on-Demand applications. The operation of multiple servers is coordinated with the assistance of an analytical framework that provides closed-form solutions to the content partitioning and scheduling problem, even under the presence of packet losses. The problem of mapping clients to servers is solved with a genetic algorithm, that manages to provide adequate, near-optimum solutions with a minimum of overhead. While previous studies focused on the static behavior of such a system, i.e. fixed a-priori known number of N servers and K clients commencing operation at the same time instance, this paper focuses on the dynamic behavior of such a system over a period of time with clients coming and going at random intervals. The paper includes a rigorous simulation study that shows how the system behaves in terms of a variety of metrics, including the average access time over all the requested media, in response to differences in the client arrival rate or the consumed server bandwidth. As it is shown, the proposed platform exhibits excellent performance characteristics that surpass traditional approaches that treat clients individually. This has been verified to be true up to extreme system loads, proving the scalability of the proposed content delivery scheme. The significance of our findings also stems from the assumption of unreliable communications, a first for the study of complete systems in this domain.