| Budget Amount *help |
¥3,600,000 (Direct Cost: ¥3,600,000)
Fiscal Year 2003: ¥1,500,000 (Direct Cost: ¥1,500,000)
Fiscal Year 2002: ¥2,100,000 (Direct Cost: ¥2,100,000)
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| Research Abstract |
In this work, we propose an intelligent Call Admission Control (CAC) and routing framework which is based on cooperative agents. The proposed framework is based on Distributed Artificial Intelligence (DAI) approach, which deals with design of artificial agents to develop intelligent systems. We introduce two types of agents : simple and intelligent agents. The intelligent agents are based on Fuzzy Logic (FL) and Genetic Algorithm (GA). The proposed strategy operates as follows. After a FL based GAG agent has decided to accept a connection in the network, a GA based IntraD routing agent is activated to find a feasible path. The proposed routing algorithm is a combination of source and distributed routing. It uses source routing inside a domain and hop-by--hop routing for inter-domain. In this report, we proposed and evaluated by simulations the performance of the Fuzzy Admission Controller (FAC), IntraD agent and ARGAQ method. From the evaluation results we conclude as follows. The perf
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ormance evaluation of proposed FAC, scheme showed that the Fuzzy Equivalent Capacity Estimator (FECE) had a good equivalent capacity estimation compared with conventional methods ; the combination of FECE and stationary approximation give a more accurate estimation of equivalent capacity ; and FAC scheme has a better admission region than the equivalent capacity method. The proposed agent-based routing and CAC framework have the following characteristics : integration of CAC and routing, combination of source and distributed routing, reduction of search space, is adaptive, flexible and intelligent, can avoid flooding, can avoid rooting loops, and is scalable. The IntraD algorithm has the following features : it uses a novel gene coding method, has an efficient search, and has easy genetic operations compared with GLBR. The performance evaluation of proposed ARGAQ method showed that ARGAQ has faster response time compared with GLBRQ method ; ARGAQ has simple genetic operations and can support two QoS parameters ; and ARGAQ can find better QoS routes than ARGA method. As future works, we would like to investigate the implementation issues of FAC scheme and GA-based routing algorithms in real environments. We intend to implement the IntraD agent in a parallel GA architecture. Less
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