Application of Cooperative Agents for Routing in Large-scale High-speed Networks

在这项工作中，我们提出了一个基于合作代理的智能呼叫接纳控制（CAC）和路由框架。该框架基于分布式人工智能（DAI）方法，该方法处理人工代理的设计以开发智能系统。我们介绍两种类型的代理：简单代理和智能代理。基于模糊逻辑（FL）和遗传算法（GA）的智能代理。拟议的战略运作如下。当基于FL的GAG代理决定接受网络中的连接后，基于GA的IntraD路由代理被激活以寻找可行的路径。提出的路由算法是源路由和分布式路由的结合。它在域内使用源路由，在域间使用逐跳路由。在本报告中，我们提出并通过仿真评估了模糊接纳控制器（FAC）、IntraD代理和ARGAQ方法的性能。从评价结果可以得出如下结论：对所提出的FAC方案进行了性能评价，结果表明，模糊等效容量估计器（FECE）与传统的等效容量估计方法相比具有较好的等效容量估计效果；FECE与平稳近似相结合，可以更准确地估计等效容量；FAC方案比等效容量法具有更好的接纳区域。提出的基于agent的路由和CAC框架具有以下特点：集成了CAC和路由，源路由和分布式路由相结合，减少了搜索空间，具有自适应、灵活和智能、避免泛洪、避免生根循环和可扩展性。IntraD算法具有以下特点：采用了一种新颖的基因编码方法，与GLBR相比具有搜索效率高、遗传操作简单等特点。对ARGAQ方法的性能评价表明，与GLBRQ方法相比，ARGAQ方法具有更快的响应时间；ARGAQ遗传操作简单，可支持两个QoS参数；与ARGA方法相比，ARGAQ方法可以找到更好的QoS路由。作为未来的工作，我们希望研究FAC方案和基于ga的路由算法在实际环境中的实现问题。我们打算在并行遗传算法架构中实现IntraD代理。少

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 … More 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