• Journal of Internet Computing and Services
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• v.6 no.6
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• pp.127-137
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• 2005

This paper presents implementation of client-based VOD service using multicast delivery, Conventional system provide server-based system in multicast delivery but implemented system provides on-demand client-based multicast system. The Multicast Agent Scheduler aggregates clients' request and it generate multicast group addresses and port numbers according to requested video items and service request time. Then it transmits immediately multicast address to VOD server and client who request service. And then VOD server transmits requested streams with a multicast group address and the client joins the group automatically. The Multicast Agent Scheduler assigns the same multicast group address when other clients request an identical video within the same scheduling duration. The proposed system can reduce load of server and support many clients at the same time and applies it to WMT(window media technology) of Microsoft.

• The Journal of Engineering Research
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• v.6 no.2
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• pp.11-22
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• 2004

A distributed constraint satisfaction problem (distributed CSP) is a constraint satisfaction problem(CSP) in which variables and constraints are distributed among multiple automated agents. ACSP is a problem to find a consistent assignment of values to variables. Even though the definition of a CSP is very simple, a surprisingly wide variety of AI problems can be formalized as CSPs. Similarly, various application problems in DAI (Distributed AI) that are concerned with finding a consistent combination of agent actions can be formalized as distributed CAPs. In recent years, many new backtracking algorithms for solving distributed CSPs have been proposed. But most of all, they have common drawbacks that the algorithm assumes the priority of agents is static. In this thesis, we establish a basic algorithm for solving distributed CSPs called dynamic priority search algorithm that is more efficient than common backtracking algorithms in which the priority order is static. In this algorithm, agents act asynchronously and concurrently based on their local knowledge without any global control, and have a flexible organization, in which the hierarchical order is changed dynamically, while the completeness of the algorithm is guaranteed. And we showed that the dynamic priority search algorithm can solve various problems, such as the distributed 200-queens problem, the distributed graph-coloring problem that common backtracking algorithm fails to solve within a reasonable amount of time. The experimental results on example problems show that this algorithm is by far more efficient than the backtracking algorithm, in which the priority order is static. The priority order represents a hierarchy of agent authority, i.e., the priority of decision-making. Therefore, these results imply that a flexible agent organization, in which the hierarchical order is changed dynamically, actually performs better than an organization in which the hierarchical order is static and rigid. Furthermore, we describe that the agent can be available to hold multiple variables in the searching scheme.