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

在这项工作中，我们提出了一个智能呼叫接纳控制（CAC）和路由框架，这是基于合作代理。建议的框架是基于分布式人工智能（DAI）的方法，它涉及人工代理开发智能系统的设计。我们介绍两种类型的代理：简单和智能代理。智能代理是基于模糊逻辑（FL）和遗传算法（GA）。拟议战略的运作方式如下。在基于FL的GAG代理决定接受网络中的连接后，基于GA的IntraD路由代理被激活以找到可行路径。它在域内使用源路由，在域间使用逐跳路由。在这份报告中，我们提出了模糊接纳控制器（FAC），IntraD代理和ARGAQ方法的性能和模拟评估。从评估结果中，我们得出如下结论。The perf ...更多信息 对FAC方案的显著性评价表明，与传统方法相比，模糊等效容量估计法（FECE）具有较好的等效容量估计效果; FECE与平稳近似法相结合，能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域。提出的基于Agent的路由和CAC框架具有以下特点：CAC与路由的集成、源路由与分布式路由的结合、搜索空间的缩小、自适应性强、灵活智能、能避免泛洪、能避免根循环、可扩展性好。与GLBR算法相比，IntraD算法具有以下特点：采用了一种新的基因编码方法，搜索效率高，遗传操作简单。对ARGAQ算法的性能评估表明，与GLBRQ算法相比，ARGAQ算法具有更快的响应时间; ARGAQ算法具有简单的遗传操作，可以支持两个QoS参数; ARGAQ算法能够找到比ARGA算法更好的QoS路由。作为未来的工作，我们想调查的实现问题的FAC计划和GA为基础的路由算法在真实的环境。我们打算在并行GA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