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Event Driven Network Simulation InCombined Searching Technique Used To Speed Up Packet Transmission In Computer Networks DB Routing Algorithm

机译:事件驱动网络仿真插入型搜索技术用于加快计算机网络中的数据包传输DB路由算法

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This paper presents a new approach for solving network routing optimization problems. In particular, the goal is to optimize the traffic in the network structured event-driven systems as well as to provide means for efficient adaptation of the system to changes in the environment - i.e. when some nodes and/or links fail. Many network routing optimization problems belong to the class of NP hard problems, which can only be solved by using some heuristic approach. This Paper provides a path to an incoming packet much faster than existing routing algorithms. This is basically an AI Concept, which is useful to get from the source to the destination. DFS (depth first search) and BFS (breath first search) searching techniques. A routing algorithm. DB Routing is based on a general-purpose metaheuristic named Ant Colony Optimization, which is a framework for building ant-inspired algorithms. DB is applied as the routing algorithm in a simulated packet-switched point-to-point network. It is investigated whether DB is able to obtain an increase in speed of transmission when packets are sent between two distinct nodes. To this end, it is investigated how prioritizing different heuristics effect the quality of the routing performed. It is concluded that DB behaves differently depending on the relative priority of positive feedback negative feedback and local heuristics, and that it is possible to adjust the parameters to achieve distribution of traffic over several paths when the network is heavily loaded
机译:本文介绍了一种解决网络路由优化问题的新方法。特别是,目标是优化网络结构化事件驱动系统中的流量,以及提供用于有效地适应系统的装置 - 即,当某些节点和/或链接失败时。许多网络路由优化问题属于NP硬盘类,只能通过使用一些启发式方法来解决。本文提供了传入数据包的路径,比现有路由算法快得多。这基本上是AI概念,它很有用来从源到目的地。 DFS(深度第一搜索)和BFS(呼吸首先搜索)搜索技术。路由算法。 DB路由基于一个名为Ant Colony优化的通用成分型,这是构建蚂蚁启发算法的框架。 DB应用于模拟分组交换点对点网络中的路由算法。调查DB是否能够在两个不同节点之间发送数据包时获得传输速度的增加。为此,调查了不同启发式效果的优先考虑了所执行的路由的质量。得出结论,DB的行为不同,具体取决于正反馈负反馈和当地启发式的相对优先级,并且可以调整参数,以在网络大量加载时在几个路径上实现流量的分布

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