In a circuit-switched network, traffic can be characterized by several factors that define how communication resources are allocated and utilized during a connection. The amount of traffic basically determines how frequently connection requests arrive, how long the setup connection remains active, and the bandwidth used. The Poisson Arrival Process models traffic arrival events at random intervals. It assumes that events happen independently of one another. This model is ideal for simulating traffic in networks where arrivals happen independently and randomly, such as the start of phone calls, data requests, or packet transmissions. The Poisson Arrival Process and uniformly choosing source and destination pair is been used most commonly by researchers to generate traffic in a network to test various promising routing and spectrum assignment algorithms. It checks the algorithm in uniformly loaded conditions and estimate its baseline performance. In real real-world scenario, a bunch of network nodes can start experiencing heavy data traffic compared to the rest of the network. This can lead to latency issues, or even outages if the network is not optimized to handle the load at these nodes which are also called hotspots. In other terms, hotspot in a network is an area or set of nodes within the network that have a higher likelihood of being involved in communication or data transmission compared to other areas. In this paper, we have tried to find what are the various factors involved in increasing the blocking probability in hotspot traffic scenarios. We have also compared the results with the uniform traffic load conditions in same topology.

翻译：在电路交换网络中，流量可通过若干决定连接期间通信资源分配与利用方式的特征参数进行描述。流量大小本质上决定了连接请求的到达频率、已建立连接的持续活动时间以及所占用的带宽。泊松到达过程通过随机间隔建模流量到达事件，其假设事件间相互独立。该模型适用于模拟到达事件独立随机发生的网络流量，例如电话呼叫发起、数据请求或数据包传输等场景。研究人员最常采用泊松到达过程结合均匀选择的源-目的节点对来生成网络流量，以测试各类具有潜力的路由与频谱分配算法。该方法可在均匀负载条件下检验算法性能并评估其基准表现。在实际网络场景中，部分网络节点可能较其他节点承受更重的数据流量，若网络未针对这些称为"热点"的节点进行负载优化，将导致延迟问题甚至服务中断。换言之，网络热点是指网络中相较于其他区域更频繁参与通信或数据传输的区域或节点集合。本文旨在探究热点流量场景中导致阻塞概率升高的各类影响因素，并在相同拓扑结构下将分析结果与均匀流量负载条件进行对比。