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 Big Data Mining and Analytics  2018, Vol. 1 Issue (1): 47-56    DOI: 10.26599/BDMA.2018.9020005
Online Internet Traffic Monitoring System Using Spark Streaming
Baojun Zhou, Jie Li*, Xiaoyan Wang, Yu Gu, Li Xu, Yongqiang Hu, Lihua Zhu
Baojun?Zhou and Jie?Li are with the Department of Computer Science, University of Tsukuba, Tsukuba 305-8577, Japan. E-mail: zhoubaojun@osdp.cs.tsukuba.ac.jp.
Xiaoyan?Wang is with the College of Engineering, Ibaraki University, Hitachi 316-8511, Japan. E-mail: xiaoyan.wang.shawn@vc.ibaraki.ac.jp.
Yu?Gu is with the School of Computer and Information, Hefei University of Technology, Hefei 230601, China. E-mail: yugu.bruce@ieee.org.
Li?Xu is with the College of Mathematics and Computer Science, Fujian Normal University, Fuzhou 350007, China. E-mail: xuli@fjnu.edu.cn.
Yongqiang Hu and Lihua Zhu are with the Institute of Scientific and Technical Information of Qinghai, Xining 810008, China. E-mail: yqhua@163com; zlh97330@163.com.
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Abstract

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.

Received: 11 August 2017      Published: 08 January 2020
Corresponding Authors: Jie Li
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 Cite this article: Baojun Zhou, Jie Li, Xiaoyan Wang, Yu Gu, Li Xu, Yongqiang Hu, Lihua Zhu. Online Internet Traffic Monitoring System Using Spark Streaming. Big Data Mining and Analytics, 2018, 1(1): 47-56. URL:
 Fig. 1 Architecture of the proposed online monitoring system. Table?1 Various useful transformation Spark Streaming APIs. Fig. 2 Typical TCP keep-alive. Fig. 3 System structure. Table?2 Configurations for each component. Fig. 4 Network performance measured by our system. Fig. 5 Performance statistics of stream processor in Spark UI, where processing time is the time taken to process all jobs for a batch, and scheduling delay is the time to ship the jobs from scheduler to executor. Fig. 6 System performance changes when a slave crashes.
 [1] Cisco Visual Networking Index, Forecast and methodology, 2016-2021, White Paper, San Jose, CA, USA: Cisco, 2016. [2] Lee Y., Kang W., and Son H., An Internet traffic analysis method with MapReduce, in Proc. 2010 IEEE/IFIP Network Operations and Management Symposium Workshops (NOMS Wksps), Osaka, Japan, 2010, pp. 357-361. [3] Brauckhoff D., Tellenbach B., Wagner A., May M., and Lakhina A., Impact of packet sampling on anomaly detection metrics, in Proc. 6th ACM SIGCOMM Conf. Int. Measurement, Rio de Janeriro, Brazil, 2006, pp. 159-164. [4] Qiao Y. Y., Lei Z. M., Yuan L., and Guo M. J., Offline traffic analysis system based on Hadoop, J. China Univ. Posts Telecommun., vol. 20, no. 5, pp. 97-103, 2013. [5] Hadoop, , 2017 [6] Kambatla K., Kollias G., Kumar V., and Grama A., Trends in big data analytics, J. Parallel Distrib. Comput., vol. 74, no. 7, pp. 2561-2573, 2014. [7] Apache Spark, , 2017. [8] Zaharia M., Chowdhury M., Franklin M. J., Shenker S., and Stoica I., Spark: Cluster computing with working sets, in Proc. 2nd USENIX Conf. Hot Topics in Cloud Computing, Boston, MA, USA, 2010, p. 10. [9] Liu J., Liu F., and Ansari N., Monitoring and analyzing big traffic data of a large-scale cellular network with Hadoop, IEEE Netw., vol. 28, no. 4, pp. 32-39, 2014. [10] Lee Y. and Lee Y., Toward scalable internet traffic measurement and analysis with Hadoop, ACM SIGCOMM Comput. Commun. Rev., vol. 43, no. 1, pp. 5-13, 2013. [11] Chen Z. J., Xu G. B., Mahalingam V., Ge L. Q., Nguyen J., Yu W., and Lu C., A cloud computing based network monitoring and threat detection system for critical infrastructures, Big Data Res., vol. 3, pp. 10-23, 2016. [12] Gupta A., Birkner R., Canini M., Feamster N., Mac-Stoker C., and Willinger W., Network monitoring as a streaming analytics problem, in Proc. 15th ACM Workshop on Hot Topics in Networks, Atlanta, GA, USA, 2016, pp. 106-112. [13] Karimi A. M., Niyaz Q., Sun W. Q., Javaid A. Y., and Devabhaktuni V. K., Distributed network traffic feature extraction for a real-time IDS, in Proc.2016 IEEE Int. Conf. Electro Information Technology (EIT), Grand Forks, ND, USA, 2016, pp. 522-526. [14] Chen C. L. P. and Zhang C. Y., Data-intensive applications, challenges, techniques and technologies: A survey on big data, Inf. Sci., vol. 275, pp. 314-347, 2014. [15] Shahrivari S., Beyond batch processing: Towards real-time and streaming big data, Computers, vol. 3, no. 4, pp. 117-129, 2014. [16] Paxson V., Bro: A system for detecting network intruders in real-time, Comput. Netw., vol. 31, nos. 23&24, pp. 2435-2463, 1999. [17] Roesch M., Snort-lightweight intrusion detection for networks, in Proc. 13th USENIX Conf. System Administration, Seattle, WA, USA, 1999, pp. 229-238. [18] Suricata, , 2017 . [19] Kafka performance, , 2017. [20] Spark Streaming, , 2017. [21] Acknowledgment ambiguity, , 2017.
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