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Active Network Modelling and Simulation: A Behavioural Approach

机译:主动网络建模与仿真:行为方法

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摘要

Network environments become more and more complex in terms of new services, quality of service to handle and user demand for increasing throughput. All these facts have to be performed in real time (or almost), and consequently, network management and control are difficult to realise in this continuously changing environment. The network is now unpredictable and there is a real lack in the modelisation and simulation tools to handle this dynamicity. In this context, agents and multi-agent systems seem to be good candidates to provide a way to model and to manage the network dynamicity. This is due to the fact that agents can perform tasks in an autonomous, distributed, adaptable and cooperative manner. Our goal here is to model an active network by using a multi-agent approach and more precisely a behaviour based agent approach. Instead of representing the actions of the network elements by complex algorithms, we rather describe them by their behaviours which are usually simple and familiar words, inspired in part from human behaviour. On this basis, we have made simulations of an active network environment, and implemented different combinations of four individual behaviours by using a multi-agent platform called Swarm. Results of these simulations are significant and show that we do not loose important packets (with priority) any more when we introduce a behaviour in the nodes.
机译:在新服务方面,网络环境变得越来越复杂,提供服务质量和用户提高吞吐量的需求。所有这些事实都必须实时(或几乎)进行,因此,在这种不断变化的环境中难以实现网络管理和控制。网络现在是不可预测的,并且在造型和仿真工具中存在真正缺乏,以处理这种动态性。在这种情况下,代理商和多代理系统似乎是良好的候选人,以提供模型和管理网络动态的方式。这是由于代理可以以自主,分布,适应性和合作方式执行任务。我们这里的目标是通过使用多种代理方法和更精确的基于行为的代理方法来模拟活动网络。而不是代表复杂算法代表网络元素的动作,而是通过他们的行为来描述它们,这些行为通常是简单且熟悉的单词,部分来自人类行为。在此基础上,我们已经模拟了主动网络环境,并通过使用称为Swarm的多代理平台来实现四个单独行为的不同组合。这些模拟的结果是重要的,并且显示在我们在节点中的行为引入行为时,我们不会失去重要数据包(优先级)。

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