Selecting among replicated batching video-on-demand servers
NOSSDAV '02 Proceedings of the 12th international workshop on Network and operating systems support for digital audio and video
A proactive tree recovery mechanism for resilient overlay multicast
IEEE/ACM Transactions on Networking (TON)
Server selection in large-scale video-on-demand systems
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Exploiting application workload characteristics to accurately estimate replica server response time
OTM'05 Proceedings of the 2005 Confederated international conference on On the Move to Meaningful Internet Systems - Volume >Part I
A frame for selecting replicated multicast servers using genetic algorithm
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part II
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We formulate and investigate fundamental problems that arise when multicast servers, that deliver content to multiple clients simultaneously, are replicated to enhance scalability and performance. Our study consists of two parts. First, we consider the problem under the assumption that the multicast clients are static for the duration of the multicast content distribution session. In this context, we examine two models for server behavior: fixed-rate servers, which transmit at a constant rate, and rate-adaptive servers, which adapt their transmission rate based on network conditions and/or feedback from clients. In both cases, we show that general versions of the client assignment problems are NP-hard. We then develop and evaluate efficient algorithms for interesting special cases, as well as heuristics for general cases. Second, we consider the case in which the set of clients changes dynamically during the multicast content distribution session. We again consider both fixed-rate and rate-adaptive servers. We formulate the problem as a Markov decision process, capturing the costs associated with trees, as well as the transition costs to dynamically change the trees. We use the properties of optimal solutions for small examples to develop a set of dynamic server selection heuristics.