Usman, Muhammad
(School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Korea)
,
Ahmad Rathore, Muhammad
(School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Korea)
,
Kim, JongWon
(School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Korea)
Modern information communication technologies (ICT) infrastructures are getting complicated to cope with the various demands needed to accommodate the emerging technology paradigms such as cloud, software-defined networking (SDN), and internet of things (IoT). Visibility is essential for the effecti...
Modern information communication technologies (ICT) infrastructures are getting complicated to cope with the various demands needed to accommodate the emerging technology paradigms such as cloud, software-defined networking (SDN), and internet of things (IoT). Visibility is essential for the effective operation of such modern ICT infrastructures to easily pinpoint server faults, network bottlenecks, and application performance troubles. Even though many conventional monitoring solutions are now available, multi-layer visibility is still limited when operating such complicated infrastructures. To address this particular limitation, a futuristic multi-layer visibility framework denoted as SmartX multi-view visibility framework (MVF), is proposed for unifying the visibility of underlay, physical and virtual resources, flow, and workload layers. To unify multi-layer visibility, this paper presents a comprehensive extension of SmartX MVF with flow-centric visibility for simultaneously monitoring physical-virtual resources, flows classification, and visualization to eventually assist secured operation of SDN-enabled multisite cloud infrastructure. Flow-centric visibility design has five main components (1) a lightweight network packets-precise flows visibility collection component, (2) a visibility data aggregation and tagging component, (3) a multi-layer visibility data integration component, (4) a non-learning-based network packets flows classification component, and (5) a visualization component. Furthermore, a prototype implementation of SmartX MVF with flow-centric visibility is deployed in an SDN-enabled multisite cloud playground to verify the proposed multi-view visibility of fine-grained flow-aware physical-virtual resources.
Modern information communication technologies (ICT) infrastructures are getting complicated to cope with the various demands needed to accommodate the emerging technology paradigms such as cloud, software-defined networking (SDN), and internet of things (IoT). Visibility is essential for the effective operation of such modern ICT infrastructures to easily pinpoint server faults, network bottlenecks, and application performance troubles. Even though many conventional monitoring solutions are now available, multi-layer visibility is still limited when operating such complicated infrastructures. To address this particular limitation, a futuristic multi-layer visibility framework denoted as SmartX multi-view visibility framework (MVF), is proposed for unifying the visibility of underlay, physical and virtual resources, flow, and workload layers. To unify multi-layer visibility, this paper presents a comprehensive extension of SmartX MVF with flow-centric visibility for simultaneously monitoring physical-virtual resources, flows classification, and visualization to eventually assist secured operation of SDN-enabled multisite cloud infrastructure. Flow-centric visibility design has five main components (1) a lightweight network packets-precise flows visibility collection component, (2) a visibility data aggregation and tagging component, (3) a multi-layer visibility data integration component, (4) a non-learning-based network packets flows classification component, and (5) a visualization component. Furthermore, a prototype implementation of SmartX MVF with flow-centric visibility is deployed in an SDN-enabled multisite cloud playground to verify the proposed multi-view visibility of fine-grained flow-aware physical-virtual resources.
참고문헌 (46)
Kim Improving network management with software defined networking IEEE Commun. Mag. 2013 10.1109/MCOM.2013.6461195 51 114
(2019, March 30). SmartFIRE-NITlab-Network. Available online: https://nitlab.inf.uth.gr/NITlab/index.php/projects/40-projects/current/444-smartfire.html.
Risdianto Prototyping Media Distribution Experiments over OF@TEIN SDN-enabled Testbed Proc. Asia-Pac. Adv. Netw. 2014 38 12
10.1109/NETSOFT.2017.8004242 Usman, M., Risdianto, A.C., Han, J., Kang, M., and Kim, J. (2017, January 3-7). SmartX multiview visibility framework leveraging open-source software for SDN-cloud playground. Proceedings of the 2017 IEEE Conference on Network Softwarization (NetSoft), Bologna, Italy.
Zhang Building and Operating Distributed SDN-Cloud Testbed with Hyper-Convergent SmartX Boxes Cloud Computing 2016 Volume 167 224
Sefraoui OpenStack: Toward an Open-source Solution for Cloud Computing Int. J. Comput. Appl. 2012 55 38
Risdianto OF@TEIN: A Community Effort towards Open/Shared SDN-Cloud Virtual Playground Proc. Asia-Pac. Adv. Netw. 2015 40 22
10.1093/comjnl/bxy103 Usman, M., Risdianto, A.C., Han, J., and Kim, J. (2018). Interactive Visualization of SDN-Enabled Multisite Cloud Playgrounds Leveraging SmartX MultiView Visibility Framework. Comput. J.
Mohan Active and Passive Network Measurements: A Survey Int. J. Comput. Sci. Inf. Technol. 2011 2 1372
(2019, March 31). Data Aggregation. Available online: https://www.ibm.com/support/knowledgecenter/en/SSBNJ7_1.4.2/dataView/Concepts/ctnpm_dv_use_data_aggreg.html.
(2019, March 31). DataLake. Available online: https://martinfowler.com/bliki/DataLake.html.
(2019, March 31). Data Integration. Available online: https://www.ibm.com/analytics/data-integration.
Dobre Internet traffic classification based on flows’ statistical properties with machine learning Int. J. Netw. Manag. 2017 10.1002/nem.1929 27 e1929
10.3390/app7030266 Kim, M., Park, Y., and Kotalwar, R. (2017). Robust and Agile System against Fault and Anomaly Traffic in Software Defined Networks. Appl. Sci., 7.
(2019, March 31). New Relic. Available online: https://newrelic.com/.
(2019, March 31). Dynatrace. Available online: https://www.dynatrace.com/.
10.1109/NOMS.2014.6838227 Chowdhury, S.R., Bari, M.F., Ahmed, R., and Boutaba, R. (2014, January 5-9). PayLess: A low cost network monitoring framework for Software Defined Networks. Proceedings of the 2014 IEEE Network Operations and Management Symposium (NOMS), Krakow, Poland.
10.1145/2775088.2775098 Risdianto, A.C., and Kim, J. (2015, January 8-10). Flow-centric Visibility Tools for OF@TEIN OpenFlow-based SDN Testbed. Proceedings of the 10th International Conference on Future Internet, Seoul, Korea.
10.1109/NOMS.2014.6838228 Van Adrichem, N.L., Doerr, C., and Kuipers, F.A. (2014, January 5-9). OpenNetMon: Network monitoring in OpenFlow Software-Defined Networks. Proceedings of the 2014 IEEE Network Operations and Management Symposium (NOMS), Krakow, Poland.
10.1145/2619239.2626310 Rasley, J., Stephens, B., Dixon, C., Rozner, E., Felter, W., Agarwal, K., Carter, J., and Fonseca, R. (2014, January 17-22). Planck: Millisecond-scale monitoring and control for commodity networks. Proceedings of the 2014 ACM conference on SIGCOMM, Chicago, IL, USA.
Yoon Flow Wars: Systemizing the Attack Surface and Defenses in Software-Defined Networks IEEE ACM Trans. Netw. 2017 10.1109/TNET.2017.2748159 25 3514
Farhadi, H., and Nakao, A. (2013, January 25-27). Application layer flow classification in SDN. Proceedings of the 15th Asia-Pacific Network Operations and Management Symposium (APNOMS), Hiroshima, Japan.
10.1109/ICSAI.2014.7009372 Li, Y., and Li, J. (2014, January 15-17). MultiClassifier: A combination of DPI and ML for application-layer classification in SDN. Proceedings of the 2nd International Conference on Systems and Informatics (ICSAI 2014), Shanghai, China.
Barakat Flow Clustering Using Machine Learning Techniques Passive and Active Network Measurement 2004 10.1007/978-3-540-24668-8_21 Volume 3015 205
Bagui Comparison of machine-learning algorithms for classification of VPN network traffic flow using time-related features J. Cyber Secur. Technol. 2017 10.1080/23742917.2017.1321891 1 108
10.1109/AINA.2015.207 Promrit, N., and Mingkhwan, A. (2015, January 24-27). Traffic Flow Classification and Visualization for Network Forensic Analysis. Proceedings of the 29th International Conference on Advanced Information Networking and Applications (2015 IEEE), Gwangiu, Korea.
10.1109/ICTC.2017.8190996 Nam, T., and Kim, J. (2017, January 16-18). Open-source IO visor eBPF-based packet tracing on multiple network interfaces of Linux boxes. Proceedings of the 2017 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Korea.
(2019, March 31). Learn eBPF Tracing: Tutorial and Examples. Available online: http://www.brendangregg.com/blog/2019-01-01/learn-ebpf-tracing.html.
10.3390/app9061114 Li, Y., Jiang, Y., Gu, J., Lu, M., Yu, M., Armstrong, E.M., Huang, T., Moroni, D., McGibbney, L.J., and Frank, G. (2019). A Cloud-Based Framework for Large-Scale Log Mining through Apache Spark and Elasticsearch. Appl. Sci., 9.
(2019, March 31). Apache Parquet. Available online: https://parquet.apache.org/documentation/latest/.
(2019, March 31). Getting Started with sbt. Available online: https://www.scala-sbt.org/1.0/docs/Getting-Started.html.
(2019, March 31). Kibana User Guide. Available online: https://www.elastic.co/guide/en/kibana/current/introduction.html.
(2019, March 31). sFlow Overview. Available online: https://sflow.org/about/index.php.
10.1109/ICTC.2018.8539523 Shin, J.S., Usman, M., and Kim, J. (2018, January 17-19). Conceptual Verification of Multi-Level Visibility Points for SmartX MultiView Security. Proceedings of the 2018 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Korea.
Lee Inter-correlation of Resource-/Flow-Level Visibility for APM Over OF@TEIN SDN-Enabled Multi-site Cloud Quality, Reliability, Security and Robustness in Heterogeneous Networks 2017 10.1007/978-3-319-60717-7_48 Volume 199 478
Risdianto Enhanced Onos Sdn Controllers Deployment for Federated Multi-Domain Sdn-Cloud with Sd-Routing-Exchange Malays. J. Comput. Sci. 2017 10.22452/mjcs.vol30no2.5 30 134
※ AI-Helper는 부적절한 답변을 할 수 있습니다.