基于网络流量监测的移动互联网特征研究
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摘要
随着移动通信技术的发展,移动互联网用户逐年增多,多种移动网络应用已经成为人们生活的一部分。移动互联网因其场景的复杂性,流量的众多特征还未被学术界所认识。网络流量监测技术可以对移动网络流量进行保存,再借助数据挖掘等技术可以分析移动互联网的流量特征并对网络的特征值构建模型,从而为移动网络的设计和优化提供参考。本文的主要研究内容和创新点如下：
(1)将云计算技术引入网络流量监测领域,创新性的利用云计算平台实现移动网络流量的存储和分析,构建了基于Hadoop云计算的海量网络流量分析平台。该平台可以实现对网络流量数据的分布式存储,用户通过提交脚本的方式实现分布式运算,极大的便利了海量网络流量的存储和处理。相关实验也证明本文基于Hadoop云计算的海量网络流量分析平台具有非常高的效率。
平台由数据导入模块、脚本解析模块、作业执行模块三大部分构成。数据导入模块是整个系统的基础,主要负责原始网络流记录的导入,清洗和索引的建立。脚本解析模块对用户编写的脚本进行检查后转换成MapReduce任务,由作业执行模块提交到Hadoop平台进行执行。本文对各模块的流程进行了说明,并对本文设计的网络流量分析脚本Log-QL语言的设计及解析方案进行详细说明。文中还对实验室的云计算平台进行了简要说明,并利用国内真实互联网采集的流量数据进行性能测试,证明了基于Hadoop云计算的海量网络流量分析平台的高效性。
(2)采用从国内某大城市骨干节点采集的CDMA移动互联网流量和ADSL固定互联网流量,对两种网络流量特征进行对比研究。首先对根据采集自真实网络流量的流量数据进行ARIMA模型建模,选取了较为合理的网络流量样本。并进行流量特征分析研究,内容包括协议分布,网络流长分布,流持续时间分布,两种网络的业务分布等。并对P2P流媒体协议特征进行研究及建模。
研究发现,监测的CDMA网络和ADSL网络流量分布都符合ARIMA模型。CDMA网络的传输协议以TCP为主,ADSL网络UDP协议流量占比更大。CDMA网络流的平均报文长度小于ADSL网络,且CDMA网络流的流持续时间小于ADSL网络,反映了移动网络的不稳定性。此外,在CDMA网络中的流下行/上行的比例要大于ADSL网络网络。基于两种网络的不同应用的流量分布,可以看到在CDMA网络中,网页浏览类应用占主导的地位,而新媒体类的应用逐渐占据了ADSL网络中的大部分流量。最后本文对网络中重点业务P2P流媒体的节点数分布进行了研究,发现CDMA网络和ADSL网络的P2P流媒体业务节点数符合不同的分布模型。
(3)利用云计算平台对彩信业务的流量分布规律及统计特性进行研究分析,基于国内某省采集的时长为1年的彩信流量。研究内容包括移动用户维度以及不同时间维度的彩信业务流量分布规律等。通过长时间粒度和短时间粒度对监测省份的彩信业务发送量、发送时间及发送速率等内容进行分析,并对短时间粒度的2G/3G彩信、个人与非个人彩信进行分析,最后对个人彩信到达间隔构建了模型。
本文使用在现网骨干网节点部署的10G速率的流量监测设备,采集南方某省时长为1年的彩信流量。文中对彩信的服务架构及数据采集过程进行了说明。流量特征分析分为长时间粒度和短时间粒度的分析。长时间粒度包含全年彩信发送量、彩信协议类型与端口号分布、全年彩信的成功率与失败率、彩信内容长度分布、接收时长分布以及传输速率分布。发现监测网络主要使用WAP2.0协议发送彩信,彩信长度集中在30至70KB,彩信接收时间通常为10秒,彩信发送速率集中在20至40Kbps。在进行短时间粒度的分析时,选取一周的彩信流量作为分析样本,主要研究了个人彩信与非个人彩信的流量特征,2G和3G彩信流量特征。发现监测网络中97%的彩信由2G网络进行发送,3G彩信的平均传输速度是2G彩信的三倍。最后对个人彩信的到达间隔进行了建模,发现Weibull分布可以较好的对个人彩信到达间隔进行拟合。
(4)针对网络的服务质量分析的需要,本文提出了一种基于K均值算法和C4.5算法的级联式的网络质量分析算法。该算法可以适用于不同种类的网络质量监测数据,通过现网采集的网络质量测量数据进行实验,验证了该算法的有效性和高效性。
算法处理对象为本研究团队研发的“面向用户的主动网络测量系统”采集的移动网络测量数据。该系统部署在国内南方某省的多个城市的移动网络节点,进行网络质量测量。网络质量分析算法分为训练模块和分析判别模块两个模块,训练模块对历史数据进行建模,分析判别模块利用建模数据对新的测量数据进行综合判定。在进行算法效果验证时,本文进行了大量的实验选取合理的K值和C4.5算法的等距离散值,并利用KKZ算法来进行聚类中心点的初始化选取。利用多个监测数据,证明了本文提出的级联算法的高效性。且该算法适用于不同的监测数据,它可以有效的调节单一算法的缺陷,总体提升判别指标的性能。
With the rapid development of mobile communication technologies, the mobile Internet user scale is increasing year after year. In this scenario, numerous mobile services have become part of people's lives. For the complex scenes of the mobile Internet, many of the characteristics are not fully understood. With the help of network traffic monitoring techniques, and also Cloud Computing and Data Mining, we analyze the characteristics of mobile Internet, build models on network traffic traces. The major contributions of the paper are as follows.
(1)We introduced the Cloud Computing technique into the area of network traffic monitoring. We used Cloud Computing platform to perform data analysis and data storage for the first time. Our Hadoop based network traffic analysis platform supports distributed storage. Users can do distributed computing by uploading scripts. The experiments in this paper also prove that our Hadoop based network traffic analysis platform is very efficient.
Data import module, script analyzing module, task implementation module are three major modules in our analysis platform. Data import module is the basis of the platform, which takes charge of uploading network traffic. Script analyzing module interprets scripts language into queries or commands. Task implementation module submits tasks onto the Hadoop framework. The Cloud Computing cluster used in the paper was also described. Based on the network traffic captured at major nodes of China, we proved the high efficiency of the platform.
(2) We compared the network traffic characteristics of CDMA network and ADSL network based on the traffic captured at a major node in Southern China. We built an ARIMA traffic model on the two traces, in order to choose proper network traffic samples. The detailed analyses include network protocol distribution, network traffic flow duration distribution, network traffic flow length distribution etc., applications traffic distribution and characteristics of P2P streaming traffic.
We found that the average packet length of CDMA trace is smaller than the ADSL traffic trace, indicating that ADSL network can support a better access service. Besides, the download/upload ratio in CDMA network is bigger. The average TCP flow duration in ADSL network is bigger than that in CDMA network, while the standard deviation is smaller in ADSL. Based on the network traffic, we found that the new media applications are taking over the majority of the ADSL network. While in the CDMA network, Web browsing is still in the dominant position. Finally, we conducted research on the peer distribution of P2P streaming traffc, and find that the ADSL and CDMA network flows follow different distributions.
(3) We study the MMS (Multimedia Messaging Service) traffic distributions and flow characteristics based on the Cloud Computing platform. It includes traffic characteristics of user scale and different time scale etc. Through the long and short time scale traffic analyses, we analyze the MMS number distribution, transmission time, transmission rate, and build model on the personal MMS inter-arrival time.
We use a network traffic monitoring system deployed at the backbone network in Southen China to capture the MMS traffic for a year. In the paper, firstly, we describe the MMS framework and the data capturing procedure. The analyses are divided into long scale and short scale. The former one includes the numbers of MMS transmission per day, MMS protocol distribution, port distribution, Success and Failure rate and content length distribution. The latter one is based on one week MMS traffic, include the personal and non-personal MMS traffic characteristics,2G/3G traffic characteristics, personal transmission rate distributioa At last we build a model on the personal MMS inter-arrival time.
(4) In terms of network operating QoS analysis, we propose a cascading network QoS analyzing algorithm based on K-means and C4.5algorithm. The algorithm was testified to be suitable for multiple analyze requirements. Also based on the monitoring data captured from the real Internet, the algorithm was proved to be effective and efficient.
The algorithm includes training module and discriminant analysis module. The training module build module based on the historical data, the discriminant analysis module make final decision based on the module. The algorithm processes the data captured from the project "User-oriented Active Network Measurement System". The system is deployed at multiple wireless network accessing points, conducting network QoS monitoring24hours. The paper covers experiments to find a proper K and C4.5discrete value, and use the KKZ algorithm to initialize the cluster center values. Based on the six traces of monitoring data, we compared the performances of cascading network QoS analyzing algorithm, K-mean algorithm and C4.5algorithm. As a result, the cascading algorithm was highly efficient and reduces the noise of single algorithm; also it proved to be suitable to several types of monitoring data.
(1)将云计算技术引入网络流量监测领域,创新性的利用云计算平台实现移动网络流量的存储和分析,构建了基于Hadoop云计算的海量网络流量分析平台。该平台可以实现对网络流量数据的分布式存储,用户通过提交脚本的方式实现分布式运算,极大的便利了海量网络流量的存储和处理。相关实验也证明本文基于Hadoop云计算的海量网络流量分析平台具有非常高的效率。
平台由数据导入模块、脚本解析模块、作业执行模块三大部分构成。数据导入模块是整个系统的基础,主要负责原始网络流记录的导入,清洗和索引的建立。脚本解析模块对用户编写的脚本进行检查后转换成MapReduce任务,由作业执行模块提交到Hadoop平台进行执行。本文对各模块的流程进行了说明,并对本文设计的网络流量分析脚本Log-QL语言的设计及解析方案进行详细说明。文中还对实验室的云计算平台进行了简要说明,并利用国内真实互联网采集的流量数据进行性能测试,证明了基于Hadoop云计算的海量网络流量分析平台的高效性。
(2)采用从国内某大城市骨干节点采集的CDMA移动互联网流量和ADSL固定互联网流量,对两种网络流量特征进行对比研究。首先对根据采集自真实网络流量的流量数据进行ARIMA模型建模,选取了较为合理的网络流量样本。并进行流量特征分析研究,内容包括协议分布,网络流长分布,流持续时间分布,两种网络的业务分布等。并对P2P流媒体协议特征进行研究及建模。
研究发现,监测的CDMA网络和ADSL网络流量分布都符合ARIMA模型。CDMA网络的传输协议以TCP为主,ADSL网络UDP协议流量占比更大。CDMA网络流的平均报文长度小于ADSL网络,且CDMA网络流的流持续时间小于ADSL网络,反映了移动网络的不稳定性。此外,在CDMA网络中的流下行/上行的比例要大于ADSL网络网络。基于两种网络的不同应用的流量分布,可以看到在CDMA网络中,网页浏览类应用占主导的地位,而新媒体类的应用逐渐占据了ADSL网络中的大部分流量。最后本文对网络中重点业务P2P流媒体的节点数分布进行了研究,发现CDMA网络和ADSL网络的P2P流媒体业务节点数符合不同的分布模型。
(3)利用云计算平台对彩信业务的流量分布规律及统计特性进行研究分析,基于国内某省采集的时长为1年的彩信流量。研究内容包括移动用户维度以及不同时间维度的彩信业务流量分布规律等。通过长时间粒度和短时间粒度对监测省份的彩信业务发送量、发送时间及发送速率等内容进行分析,并对短时间粒度的2G/3G彩信、个人与非个人彩信进行分析,最后对个人彩信到达间隔构建了模型。
本文使用在现网骨干网节点部署的10G速率的流量监测设备,采集南方某省时长为1年的彩信流量。文中对彩信的服务架构及数据采集过程进行了说明。流量特征分析分为长时间粒度和短时间粒度的分析。长时间粒度包含全年彩信发送量、彩信协议类型与端口号分布、全年彩信的成功率与失败率、彩信内容长度分布、接收时长分布以及传输速率分布。发现监测网络主要使用WAP2.0协议发送彩信,彩信长度集中在30至70KB,彩信接收时间通常为10秒,彩信发送速率集中在20至40Kbps。在进行短时间粒度的分析时,选取一周的彩信流量作为分析样本,主要研究了个人彩信与非个人彩信的流量特征,2G和3G彩信流量特征。发现监测网络中97%的彩信由2G网络进行发送,3G彩信的平均传输速度是2G彩信的三倍。最后对个人彩信的到达间隔进行了建模,发现Weibull分布可以较好的对个人彩信到达间隔进行拟合。
(4)针对网络的服务质量分析的需要,本文提出了一种基于K均值算法和C4.5算法的级联式的网络质量分析算法。该算法可以适用于不同种类的网络质量监测数据,通过现网采集的网络质量测量数据进行实验,验证了该算法的有效性和高效性。
算法处理对象为本研究团队研发的“面向用户的主动网络测量系统”采集的移动网络测量数据。该系统部署在国内南方某省的多个城市的移动网络节点,进行网络质量测量。网络质量分析算法分为训练模块和分析判别模块两个模块,训练模块对历史数据进行建模,分析判别模块利用建模数据对新的测量数据进行综合判定。在进行算法效果验证时,本文进行了大量的实验选取合理的K值和C4.5算法的等距离散值,并利用KKZ算法来进行聚类中心点的初始化选取。利用多个监测数据,证明了本文提出的级联算法的高效性。且该算法适用于不同的监测数据,它可以有效的调节单一算法的缺陷,总体提升判别指标的性能。
With the rapid development of mobile communication technologies, the mobile Internet user scale is increasing year after year. In this scenario, numerous mobile services have become part of people's lives. For the complex scenes of the mobile Internet, many of the characteristics are not fully understood. With the help of network traffic monitoring techniques, and also Cloud Computing and Data Mining, we analyze the characteristics of mobile Internet, build models on network traffic traces. The major contributions of the paper are as follows.
(1)We introduced the Cloud Computing technique into the area of network traffic monitoring. We used Cloud Computing platform to perform data analysis and data storage for the first time. Our Hadoop based network traffic analysis platform supports distributed storage. Users can do distributed computing by uploading scripts. The experiments in this paper also prove that our Hadoop based network traffic analysis platform is very efficient.
Data import module, script analyzing module, task implementation module are three major modules in our analysis platform. Data import module is the basis of the platform, which takes charge of uploading network traffic. Script analyzing module interprets scripts language into queries or commands. Task implementation module submits tasks onto the Hadoop framework. The Cloud Computing cluster used in the paper was also described. Based on the network traffic captured at major nodes of China, we proved the high efficiency of the platform.
(2) We compared the network traffic characteristics of CDMA network and ADSL network based on the traffic captured at a major node in Southern China. We built an ARIMA traffic model on the two traces, in order to choose proper network traffic samples. The detailed analyses include network protocol distribution, network traffic flow duration distribution, network traffic flow length distribution etc., applications traffic distribution and characteristics of P2P streaming traffic.
We found that the average packet length of CDMA trace is smaller than the ADSL traffic trace, indicating that ADSL network can support a better access service. Besides, the download/upload ratio in CDMA network is bigger. The average TCP flow duration in ADSL network is bigger than that in CDMA network, while the standard deviation is smaller in ADSL. Based on the network traffic, we found that the new media applications are taking over the majority of the ADSL network. While in the CDMA network, Web browsing is still in the dominant position. Finally, we conducted research on the peer distribution of P2P streaming traffc, and find that the ADSL and CDMA network flows follow different distributions.
(3) We study the MMS (Multimedia Messaging Service) traffic distributions and flow characteristics based on the Cloud Computing platform. It includes traffic characteristics of user scale and different time scale etc. Through the long and short time scale traffic analyses, we analyze the MMS number distribution, transmission time, transmission rate, and build model on the personal MMS inter-arrival time.
We use a network traffic monitoring system deployed at the backbone network in Southen China to capture the MMS traffic for a year. In the paper, firstly, we describe the MMS framework and the data capturing procedure. The analyses are divided into long scale and short scale. The former one includes the numbers of MMS transmission per day, MMS protocol distribution, port distribution, Success and Failure rate and content length distribution. The latter one is based on one week MMS traffic, include the personal and non-personal MMS traffic characteristics,2G/3G traffic characteristics, personal transmission rate distributioa At last we build a model on the personal MMS inter-arrival time.
(4) In terms of network operating QoS analysis, we propose a cascading network QoS analyzing algorithm based on K-means and C4.5algorithm. The algorithm was testified to be suitable for multiple analyze requirements. Also based on the monitoring data captured from the real Internet, the algorithm was proved to be effective and efficient.
The algorithm includes training module and discriminant analysis module. The training module build module based on the historical data, the discriminant analysis module make final decision based on the module. The algorithm processes the data captured from the project "User-oriented Active Network Measurement System". The system is deployed at multiple wireless network accessing points, conducting network QoS monitoring24hours. The paper covers experiments to find a proper K and C4.5discrete value, and use the KKZ algorithm to initialize the cluster center values. Based on the six traces of monitoring data, we compared the performances of cascading network QoS analyzing algorithm, K-mean algorithm and C4.5algorithm. As a result, the cascading algorithm was highly efficient and reduces the noise of single algorithm; also it proved to be suitable to several types of monitoring data.
引文
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