网络速度趋势预测系统的研究与实现

发布时间：2018-06-09 01:36

本文选题：速度趋势 + SVR　；参考：《北京邮电大学》2017年硕士论文

【摘要】：随着互联网快速成熟的发展,从互联网获取信息已经成为人们日常生活中获取信息的重要渠道之一。伴随着2G网络,3G网络,4G网络的逐代更新,网络的访问速度也在不断的提升,近年来国家也发布了关于提升网络速度的相关方案。在实际的应用中网络的访问会经常随着不同的访问时间而呈现出速度上的巨大差异,甚至由于此类的原因导致相关资源无法及时获取到。因此,我们需要提供一种监控手段对网络的实际状况进行评估和反馈,对接下来网络的稳定状态有一个清晰的认识。目前机器学习算法被广泛的应用到各个领域来解决分类和回归问题,常见的机器学习算法诸如支持向量回归(SVR)和神经网络都发展的很成熟。本文应用机器学习算法对网络的速度趋势进行预测。将机器学习算法应用到网络速度趋势预测当中一方面提供了预测的科学性和理论性,另一方面也促使了机器学习算法多分支快速的发展。本文的大工作如下:1、设计了一套网络速度采集模型,该采集模型会对指定运营商提供的网络速度进行采集,采集后的网络速度作为趋势预测的元数据。该采集模型对采集的功能模块进行了分割,采用云端对采集服务器进行管理和任务的分配,一方面减小了单一服务器负责全部任务的负载,另一方面也方便了日后采集服务器数量的扩展。2、提出了针对本课题的输入向量的选取方式,选取输入向量的方式有很多,本课题从前两个月的网络速度数据中提取一部分作为预测第三个月网络速度数据的输入。不同的选取方式对预测的效果影响很大,在实验对比的基础上最终确定了六维的输入向量。3、综合对比了 PSO优化的SVR和神经网络的预测效果,其中对于神经网络的选取没有固定在特定的结构上,而是在不同的实验基础上采用不同结构的神经网络进行综合对比,最终确定预测效果最好的神经网络作为与PSO优化的SVR对比的参考。4、本文的主题是网络速度趋势的预测,趋势预测本身是一个分类的问题,本文采用准确率,召回率和F1-socre作为趋势预测的一个评估标准。除此之外,本文还采用回归的方式对网络的速度进行预测,这里提供了网络速度值的参考,以MAE, MSE, MAPE作为衡量的标准,在训练的过程中以MSE作为适应度函数。5、综合以上实现了一个完整的网络速度趋势预测系统,并在实际的数据基础上进行了实验验证和性能的测试,完成了课题提出的目标。
[Abstract]:With the rapid development of the Internet, obtaining information from the Internet has become one of the important channels for people to obtain information in their daily life. With the generation update of 2G network and 3G network, the access speed of the network is improving constantly. In recent years, the country has also issued the related plan to improve the network speed. In practical applications, network access often presents a huge difference in speed with different access times, even due to such reasons, related resources can not be obtained in time. Therefore, we need to provide a monitoring means to evaluate and feedback the actual situation of the network, and have a clear understanding of the stability of the network. At present machine learning algorithms are widely used in various fields to solve classification and regression problems. Common machine learning algorithms such as support vector regression (SVR) and neural networks are developed very mature. In this paper, the machine learning algorithm is used to predict the speed trend of the network. The application of machine learning algorithm to network speed trend prediction not only provides scientific and theoretical prediction, but also promotes the rapid development of multi-branch machine learning algorithm. The main work of this paper is as follows: 1. A set of network speed acquisition model is designed. The collection model will collect the network speed provided by the designated operator and the network speed will be used as the metadata to predict the trend. The collection model divides the function module of the collection and uses the cloud to manage and distribute the tasks of the collection server. On the one hand, it reduces the load of the single server which is responsible for all the tasks. On the other hand, it also facilitates the expansion of the number of collection servers in the future. 2. The selection of input vectors for this topic is proposed. There are many ways to select input vectors. This paper extracts part of the network speed data from the first two months as input to predict the third month network speed data. Different selection methods have great influence on the effect of prediction. On the basis of experimental comparison, the six-dimensional input vector .3is finally determined, and the prediction effect of PSO optimized SVR and neural network is compared synthetically. The selection of neural network is not fixed on the specific structure, but on the basis of different experiments, the neural network with different structure is used for comprehensive comparison. Finally determine the best prediction effect of neural network as a reference compared with PSO optimized SVR. The theme of this paper is network speed trend prediction, trend prediction itself is a classification problem, this paper adopts accuracy. Recall rates and F 1-socre are used as an evaluation criterion for trend forecasting. In addition, this paper also uses regression method to predict the speed of the network, which provides a reference for the network speed value, with mae, MSE, MAPE as the measurement standard, In the process of training, MSE is taken as the fitness function. 5, a complete network speed trend prediction system is realized, and the experimental verification and performance test are carried out on the basis of the actual data, and the target proposed by the project is completed.
【学位授予单位】：北京邮电大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP18;TP393.0

【参考文献】