The Experts below are selected from a list of 17622 Experts worldwide ranked by ideXlab platform
Yongqiang Hu - One of the best experts on this subject based on the ideXlab platform.
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Online Internet Traffic monitoring system using spark streaming
Big Data Mining and Analytics, 2018Co-Authors: Baojun Zhou, Jie Li, Xiaoyan Wang, Yu Gu, Li Xu, Yongqiang HuAbstract:Owing to the explosive growth of Internet Traffic, network operators must be able to monitor the entire network situation and efficiently manage their network resources. Traditional network analysis methods that usually work on a single machine are no longer suitable for huge Traffic data owing to their poor processing ability. Big data frameworks, such as Hadoop and Spark, can handle such analysis jobs even for a large amount of network Traffic. However, Hadoop and Spark are inherently designed for offline data analysis. To cope with streaming data, various stream-processing-based frameworks have been proposed, such as Storm, Flink, and Spark Streaming. In this study, we propose an online Internet Traffic monitoring system based on Spark Streaming. The system comprises three parts, namely, the collector, messaging system, and stream processor. We considered the TCP performance monitoring as a special use case of showing how network monitoring can be performed with our proposed system. We conducted typical experiments with a cluster in standalone mode, which showed that our system performs well for large Internet Traffic measurement and monitoring.
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Online Internet Traffic Measurement and Monitoring Using Spark Streaming
GLOBECOM 2017 - 2017 IEEE Global Communications Conference, 2017Co-Authors: Baojun Zhou, Jinsong Wu, Jie Li, Yongqiang HuAbstract:Due to the explosive growth of Internet Traffic, network operators must be able to monitor the whole network situations and manage their network resources in an efficient way. Traditional network analysis method that works on a single machine are no longer suitable for this huge Traffic data due to its poor processing ability. Some big data frameworks, such as Hadoop and Spark, can handle such analysis job even for large network Traffic, but they are inherently designed for offline data analysis. In this paper, we treat the online network analysis as a stream analysis problem and use Spark Streaming to cope with the high-speed Internet Traffic data in real time. The system consists of two parts, collector and stream processor. Firstly, several collectors capture network Traffic data from switches through mirrored ports and send the packet information to a central stream processor which is a cluster running Spark Streaming. Then, the stream processor analyzes the input data streams and calculates Internet performance metrics. We take TCP performance monitoring as an example to show how network measurement can be done using the stream processing platform. Finally, we conducted typical experiments in a cluster of 3 computers with the standalone mode, showing that our system performs well in huge Internet Traffic measurement and monitoring.
Can Chen - One of the best experts on this subject based on the ideXlab platform.
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service usage classification with encrypted Internet Traffic in mobile messaging apps
IEEE Transactions on Mobile Computing, 2016Co-Authors: Yanjie Fu, Hui Xiong, Xinjiang Lu, Jin Yang, Can ChenAbstract:The rapid adoption of mobile messaging Apps has enabled us to collect massive amount of encrypted Internet Traffic of mobile messaging. The classification of this Traffic into different types of in-App service usages can help for intelligent network management, such as managing network bandwidth budget and providing quality of services. Traditional approaches for classification of Internet Traffic rely on packet inspection, such as parsing HTTP headers. However, messaging Apps are increasingly using secure protocols, such as HTTPS and SSL, to transmit data. This imposes significant challenges on the performances of service usage classification by packet inspection. To this end, in this paper, we investigate how to exploit encrypted Internet Traffic for classifying in-App usages. Specifically, we develop a system, named CUMMA, for classifying service usages of mobile messaging Apps by jointly modeling user behavioral patterns, network Traffic characteristics, and temporal dependencies. Along this line, we first segment Internet Traffic from Traffic-flows into sessions with a number of dialogs in a hierarchical way. Also, we extract the discriminative features of Traffic data from two perspectives: (i) packet length and (ii) time delay. Next, we learn a service usage predictor to classify these segmented dialogs into single-type usages or outliers. In addition, we design a clustering Hidden Markov Model (HMM) based method to detect mixed dialogs from outliers and decompose mixed dialogs into sub- dialogs of single-type usage. Indeed, CUMMA enables mobile analysts to identify service usages and analyze end-user in-App behaviors even for encrypted Internet Traffic. Finally, the extensive experiments on real-world messaging data demonstrate the effectiveness and efficiency of the proposed method for service usage classification.
Baojun Zhou - One of the best experts on this subject based on the ideXlab platform.
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Online Internet Traffic monitoring system using spark streaming
Big Data Mining and Analytics, 2018Co-Authors: Baojun Zhou, Jie Li, Xiaoyan Wang, Yu Gu, Li Xu, Yongqiang HuAbstract:Owing to the explosive growth of Internet Traffic, network operators must be able to monitor the entire network situation and efficiently manage their network resources. Traditional network analysis methods that usually work on a single machine are no longer suitable for huge Traffic data owing to their poor processing ability. Big data frameworks, such as Hadoop and Spark, can handle such analysis jobs even for a large amount of network Traffic. However, Hadoop and Spark are inherently designed for offline data analysis. To cope with streaming data, various stream-processing-based frameworks have been proposed, such as Storm, Flink, and Spark Streaming. In this study, we propose an online Internet Traffic monitoring system based on Spark Streaming. The system comprises three parts, namely, the collector, messaging system, and stream processor. We considered the TCP performance monitoring as a special use case of showing how network monitoring can be performed with our proposed system. We conducted typical experiments with a cluster in standalone mode, which showed that our system performs well for large Internet Traffic measurement and monitoring.
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Online Internet Traffic Measurement and Monitoring Using Spark Streaming
GLOBECOM 2017 - 2017 IEEE Global Communications Conference, 2017Co-Authors: Baojun Zhou, Jinsong Wu, Jie Li, Yongqiang HuAbstract:Due to the explosive growth of Internet Traffic, network operators must be able to monitor the whole network situations and manage their network resources in an efficient way. Traditional network analysis method that works on a single machine are no longer suitable for this huge Traffic data due to its poor processing ability. Some big data frameworks, such as Hadoop and Spark, can handle such analysis job even for large network Traffic, but they are inherently designed for offline data analysis. In this paper, we treat the online network analysis as a stream analysis problem and use Spark Streaming to cope with the high-speed Internet Traffic data in real time. The system consists of two parts, collector and stream processor. Firstly, several collectors capture network Traffic data from switches through mirrored ports and send the packet information to a central stream processor which is a cluster running Spark Streaming. Then, the stream processor analyzes the input data streams and calculates Internet performance metrics. We take TCP performance monitoring as an example to show how network measurement can be done using the stream processing platform. Finally, we conducted typical experiments in a cluster of 3 computers with the standalone mode, showing that our system performs well in huge Internet Traffic measurement and monitoring.
Jilali Antari - One of the best experts on this subject based on the ideXlab platform.
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Identification and Prediction of Internet Traffic Using Artificial Neural Networks
Journal of Intelligent Learning Systems and Applications, 2010Co-Authors: Samira Chabaa, Abdelouhab Zeroual, Jilali AntariAbstract:This paper presents the development of an artificial neural network (ANN) model based on the multi-layer perceptron (MLP) for analyzing Internet Traffic data over IP networks. We applied the ANN to analyze a time series of measured data for network response evaluation. For this reason, we used the input and output data of an Internet Traffic over IP networks to identify the ANN model, and we studied the performance of some training algorithms used to estimate the weights of the neuron. The comparison between some training algorithms demonstrates the efficiency and the accu-racy of the Levenberg-Marquardt (LM) and the Resilient back propagation (Rp) algorithms in term of statistical crite-ria. Consequently, the obtained results show that the developed models, using the LM and the Rp algorithms, can successfully be used for analyzing Internet Traffic over IP networks, and can be applied as an excellent and fundamental tool for the management of the Internet Traffic at different times.
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ANFIS method for forecasting Internet Traffic time series
2009 Mediterrannean Microwave Symposium (MMS), 2009Co-Authors: Samira Chabaa, Abdelouhab Zeroual, Jilali AntariAbstract:In This paper we have applied the adaptive neuro-fuzzy inference system (ANFIS) which is realized by an appropriate combination of fuzzy systems and neural networks for forecasting a set of input and output data of Internet Traffic time series. Several statistical criteria are applied to provide the effectiveness of this model. The obtained results demonstrate that the ANFIS model present a good precision in the prediction process of Internet Traffic in terms of statistical indicators. This model fits well real data and provides an effective description of network condition at different times.
Yanjie Fu - One of the best experts on this subject based on the ideXlab platform.
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service usage classification with encrypted Internet Traffic in mobile messaging apps
IEEE Transactions on Mobile Computing, 2016Co-Authors: Yanjie Fu, Hui Xiong, Xinjiang Lu, Jin Yang, Can ChenAbstract:The rapid adoption of mobile messaging Apps has enabled us to collect massive amount of encrypted Internet Traffic of mobile messaging. The classification of this Traffic into different types of in-App service usages can help for intelligent network management, such as managing network bandwidth budget and providing quality of services. Traditional approaches for classification of Internet Traffic rely on packet inspection, such as parsing HTTP headers. However, messaging Apps are increasingly using secure protocols, such as HTTPS and SSL, to transmit data. This imposes significant challenges on the performances of service usage classification by packet inspection. To this end, in this paper, we investigate how to exploit encrypted Internet Traffic for classifying in-App usages. Specifically, we develop a system, named CUMMA, for classifying service usages of mobile messaging Apps by jointly modeling user behavioral patterns, network Traffic characteristics, and temporal dependencies. Along this line, we first segment Internet Traffic from Traffic-flows into sessions with a number of dialogs in a hierarchical way. Also, we extract the discriminative features of Traffic data from two perspectives: (i) packet length and (ii) time delay. Next, we learn a service usage predictor to classify these segmented dialogs into single-type usages or outliers. In addition, we design a clustering Hidden Markov Model (HMM) based method to detect mixed dialogs from outliers and decompose mixed dialogs into sub- dialogs of single-type usage. Indeed, CUMMA enables mobile analysts to identify service usages and analyze end-user in-App behaviors even for encrypted Internet Traffic. Finally, the extensive experiments on real-world messaging data demonstrate the effectiveness and efficiency of the proposed method for service usage classification.
