Research Article
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Yazılım Tanımlı Ağlarda Ölçeklenebilir ve Verimli Bir Port Tabanlı Adaptif Kaynak İzleme Yaklaşımı

Year 2022, Volume 13, Issue 1, 9 - 26, 09.06.2022
https://doi.org/10.29048/makufebed.1003502

Abstract

Geçmişten günümüze iletişim araçlarına olan ihtiyaç artmakta, bu nedenle ağ teknolojilerindeki araç ve yöntemler gelişmektedir. Bu gelişmeye bağlı olarak ağ ölçeği ve karmaşıklığı artmakta olup geleneksel ağ teknolojisinin problemleri ortaya çıkmaktadır. Yazılım Tanımlı Ağ (YTA), bu zorlukların yönetimi ve optimizasyonu için çeşitli fırsatlar sunar. Ağ kaynaklarının izlenmesi, ağ uygulamalarına bilgi sağlamak için çok önemlidir. Bu çalışma, SDN ağları için tasarlanmış uyarlanabilir bir bağlantı noktası düzeyi bant genişliği izleme yöntemini amaçlamaktadır. Önerilen yaklaşım, yoklama tabanlı izleme paradigmasını kullanır. Ölçüm doğruluğu ve izleme yükü arasında bir denge bulunmaktadır. Bu uyarlamalı yöntemle, ölçümlerin kabul edilebilir doğruluk seviyesini korurken ek yükün azaltılması ve ayrıca ağ kaynaklarının daha verimli kullanılması amaçlanmaktadır. Önerilen uyarlamalı izleme yaklaşımı, periyodik yoklama yöntemine göre %46, PayLess yaklaşımına göre ise %6,7 daha az ek yük elde edilmiştir. Aynı zamanda bu yaklaşım, periyodik yoklama yaklaşımına göre %5,4 daha doğru bir ölçüm sağlamıştır.

References

  • Balasubramanian, V., Aloqaily, M., Reisslein, M. (2021). An SDN architecture for time sensitive industrial IoT. Computer Networks, 186; DOI: 10.1016/j.comnet.2020.107739
  • Castillo, E.F., Rendon, O.M.C., Ordonez, A., Granville, L.Z. (2020). IPro: An approach for intelligent SDN monitoring Computer Networks, 170; DOI: 10.1016/j.comnet.2020.107108
  • Chowdhury, S.R., Bari, M.F., Ahmed, R., Boutaba, R. (2014). Payless: A low cost network monitoring framework for software defined networks. Network Operations and Management Symposium (NOMS), IEEE, 1–9.
  • Floodlight Controller (2017). http://www.projectfloodlight.org. (Accessed Date: March 12, 2017)
  • Gude, N., Koponen, T., Pettit, J., Pfaff, B., Casado, M., McKeown, N., Shenker, S. (2008). NOX: towards an operating system for networks. SIGCOMM Computer Communication Review, 38(3):105-110.
  • Hernandez, E.A., Chidester, M.C., George, A.D. (2001). Adaptive sampling for network management. Journal of Network and Systems Management, 9(4): 409–434.
  • Huang, L., Zhi, X., Gao, Q., Kausar, S., Zheng, S. (2016). Design and implementation of multicast routing system over SDN and sFlow. 8th IEEE International Conference on Communication Software and Networks, 524‐529.
  • José, S.V., Pere, B.R. (2017). Reinventing NetFlow for OpenFlow Software-Defined Networks. In: arXiv preprint arXiv:1702.06803.
  • Mohan, V., Reddy, YJ., Kalpana, K. (2011). Active and passive network measurements: a survey. International Journal of Computer Science and Information Technologies, 2 (4): 1372-1385.
  • Open Networking Foundation. OpenFlow Switch Specification Version 1.5.1. 2015. https://opennetworking.org/wp-content/uploads/2014/10/openflow-switch-v1.5.1.pdf (Accessed Date: March 04, 2019).
  • Özer, H., Okumuş, İ.T. (2019). Yazılım tanımlı ağlarda izleme. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 22: 26-33.
  • Phan, XT., Martinez-Casanueva, ID., Fukuda, K. (2017). Adaptive and distributed monitoring mechanism in software-defined networks. 13th International Conference on Network and Service Management (CNSM),1–5.
  • POX Controller (2017). https://github.com/noxrepo/pox (Accessed Date: July 12, 2017)
  • Shah, S. A. R., Bae, S., Jaikar, A., Noh, S.-Y. (2016). An adaptive load monitoring solution for logically centralized sdn controller. 18th Asia-Pacific Network Operations and Management Symposium (APNOMS), 1–6.
  • Shirali-Shahreza, S., Ganjali, Y. (2013). Empowering Software Defined Network controller with packet-level information. 2013 IEEE International Conference on Communications Workshops (ICC), 1335-1339.
  • Yang, L., Ng, B., Seah, W.K., Groves, L., Singh, D. (2021). A survey on network forwarding in Software-Defined Networking. Journal of Network and Computer Applications, 176; DOI: 10.1016/j.jnca.2020.102947
  • Li, M., Chen, C., Hua, C., Guan, X. (2019). CFlow: A learning-based compressive flow statistics collection scheme for SDNs. IEEE International Conference on Communications (ICC), 1–6.
  • Liu, C., Malboubi, A., Chuah, C.-N. (2016). OpenMeasure: Adaptive flow measurement & inference with online learning in SDN. IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 47–52.
  • Su, Z., Wang, T., Xia, Y., Hamdi, M. (2014). FlowCover: low-cost flow monitoring scheme in software defined networks. IEEE GLOBECOM'14, 1956-1961.
  • Su, Z., Wang, T., Xia, Y., Hamdi, M. (2015). Cemon: a cost‐effective flow monitoring system in software defined networks. Computer Networks, 92:101‐115.
  • Terzi, D., Terzi, R., Sagiroglu, S. (2017). Big data analytics for network anomaly detection from NetFlow data. (UBMK'17) 2nd International Conference on Computer Science and Engineering, 592‐597.
  • The Mininet Platform (2018). http://mininet.org (Accessed Date: December 12, 2018)
  • Tootoonchian, A., Ghobadi, M., Ganjali, Y. (2010). OpenTM: Traffic matrix estimator for OpenFlow networks. Proceedings of the 11th International Conference on Passive and Active Measurement, 201–210.
  • Van Adrichem, N.L., Doerr, C., Kuipers, F.A. (2014). OpenNetMon: Network monitoring in openflow software-defined networks. IEEE Network Operations and Management Symposium (NOMS), DOI: 10.1109/NOMS.2014. 6838228
  • Yu, C., Lumezanu, C., Zhang, Y., Singh, V., Jiang, G., Madhyastha, HV. (2013). FlowSense: Monitoring network utilization with zero measurement cost, Passive and active measurement (PAM), 7799: 31–41.
  • Yu, M., Jose, L., Miao, R. (2013). Software defined traffic measurement with OpenSketch. The 10th USENIX Symposium on Networked Systems Design and Implementation, NSDI’13, 29–42.

A Scalable and Efficient Port-Based Adaptive Resource Monitoring Approach in Software Defined Networks

Year 2022, Volume 13, Issue 1, 9 - 26, 09.06.2022
https://doi.org/10.29048/makufebed.1003502

Abstract

The need for communication tools is increasing from the past to the present, therefore the tools and methods in network technologies are evolving. Depending on this development, network scale and complexity increase and the limitations of traditional network technology are surfaced. Software-defined Network (SDN) provides a variety of opportunities for the management and optimization of these challenges. Network resource monitoring is very important for providing information to network applications. This study introduces an adaptive port-level bandwidth monitoring method designed for SDN networks. The proposed approach uses a polling-based monitoring paradigm. There is a trade-off between measurement accuracy and monitoring overhead. With this adaptive method, it is aimed to decrease the overhead while maintaining an acceptable level of accuracy of the measurements and also to use network resources more efficiently. The proposed adaptive monitoring approach has 46% less overhead than the periodic polling method and 6.7% less overhead than the PayLess approach. At the same time, this approach is 5.4% more accurate than the periodic polling approach.

References

  • Balasubramanian, V., Aloqaily, M., Reisslein, M. (2021). An SDN architecture for time sensitive industrial IoT. Computer Networks, 186; DOI: 10.1016/j.comnet.2020.107739
  • Castillo, E.F., Rendon, O.M.C., Ordonez, A., Granville, L.Z. (2020). IPro: An approach for intelligent SDN monitoring Computer Networks, 170; DOI: 10.1016/j.comnet.2020.107108
  • Chowdhury, S.R., Bari, M.F., Ahmed, R., Boutaba, R. (2014). Payless: A low cost network monitoring framework for software defined networks. Network Operations and Management Symposium (NOMS), IEEE, 1–9.
  • Floodlight Controller (2017). http://www.projectfloodlight.org. (Accessed Date: March 12, 2017)
  • Gude, N., Koponen, T., Pettit, J., Pfaff, B., Casado, M., McKeown, N., Shenker, S. (2008). NOX: towards an operating system for networks. SIGCOMM Computer Communication Review, 38(3):105-110.
  • Hernandez, E.A., Chidester, M.C., George, A.D. (2001). Adaptive sampling for network management. Journal of Network and Systems Management, 9(4): 409–434.
  • Huang, L., Zhi, X., Gao, Q., Kausar, S., Zheng, S. (2016). Design and implementation of multicast routing system over SDN and sFlow. 8th IEEE International Conference on Communication Software and Networks, 524‐529.
  • José, S.V., Pere, B.R. (2017). Reinventing NetFlow for OpenFlow Software-Defined Networks. In: arXiv preprint arXiv:1702.06803.
  • Mohan, V., Reddy, YJ., Kalpana, K. (2011). Active and passive network measurements: a survey. International Journal of Computer Science and Information Technologies, 2 (4): 1372-1385.
  • Open Networking Foundation. OpenFlow Switch Specification Version 1.5.1. 2015. https://opennetworking.org/wp-content/uploads/2014/10/openflow-switch-v1.5.1.pdf (Accessed Date: March 04, 2019).
  • Özer, H., Okumuş, İ.T. (2019). Yazılım tanımlı ağlarda izleme. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 22: 26-33.
  • Phan, XT., Martinez-Casanueva, ID., Fukuda, K. (2017). Adaptive and distributed monitoring mechanism in software-defined networks. 13th International Conference on Network and Service Management (CNSM),1–5.
  • POX Controller (2017). https://github.com/noxrepo/pox (Accessed Date: July 12, 2017)
  • Shah, S. A. R., Bae, S., Jaikar, A., Noh, S.-Y. (2016). An adaptive load monitoring solution for logically centralized sdn controller. 18th Asia-Pacific Network Operations and Management Symposium (APNOMS), 1–6.
  • Shirali-Shahreza, S., Ganjali, Y. (2013). Empowering Software Defined Network controller with packet-level information. 2013 IEEE International Conference on Communications Workshops (ICC), 1335-1339.
  • Yang, L., Ng, B., Seah, W.K., Groves, L., Singh, D. (2021). A survey on network forwarding in Software-Defined Networking. Journal of Network and Computer Applications, 176; DOI: 10.1016/j.jnca.2020.102947
  • Li, M., Chen, C., Hua, C., Guan, X. (2019). CFlow: A learning-based compressive flow statistics collection scheme for SDNs. IEEE International Conference on Communications (ICC), 1–6.
  • Liu, C., Malboubi, A., Chuah, C.-N. (2016). OpenMeasure: Adaptive flow measurement & inference with online learning in SDN. IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 47–52.
  • Su, Z., Wang, T., Xia, Y., Hamdi, M. (2014). FlowCover: low-cost flow monitoring scheme in software defined networks. IEEE GLOBECOM'14, 1956-1961.
  • Su, Z., Wang, T., Xia, Y., Hamdi, M. (2015). Cemon: a cost‐effective flow monitoring system in software defined networks. Computer Networks, 92:101‐115.
  • Terzi, D., Terzi, R., Sagiroglu, S. (2017). Big data analytics for network anomaly detection from NetFlow data. (UBMK'17) 2nd International Conference on Computer Science and Engineering, 592‐597.
  • The Mininet Platform (2018). http://mininet.org (Accessed Date: December 12, 2018)
  • Tootoonchian, A., Ghobadi, M., Ganjali, Y. (2010). OpenTM: Traffic matrix estimator for OpenFlow networks. Proceedings of the 11th International Conference on Passive and Active Measurement, 201–210.
  • Van Adrichem, N.L., Doerr, C., Kuipers, F.A. (2014). OpenNetMon: Network monitoring in openflow software-defined networks. IEEE Network Operations and Management Symposium (NOMS), DOI: 10.1109/NOMS.2014. 6838228
  • Yu, C., Lumezanu, C., Zhang, Y., Singh, V., Jiang, G., Madhyastha, HV. (2013). FlowSense: Monitoring network utilization with zero measurement cost, Passive and active measurement (PAM), 7799: 31–41.
  • Yu, M., Jose, L., Miao, R. (2013). Software defined traffic measurement with OpenSketch. The 10th USENIX Symposium on Networked Systems Design and Implementation, NSDI’13, 29–42.

Details

Primary Language English
Subjects Engineering
Journal Section Research Paper
Authors

Hasan ÖZER> (Primary Author)
TC MİLLİ EĞİTİM BAKANLIĞI GAZİANTEP
0000-0002-0729-676X
Türkiye


İbrahim Taner OKUMUŞ>
KAHRAMANMARAŞ SÜTÇÜ İMAM ÜNİVERSİTESİ
0000-0001-9495-3133
Türkiye

Publication Date June 9, 2022
Published in Issue Year 2022, Volume 13, Issue 1

Cite

APA Özer, H. & Okumuş, İ. T. (2022). A Scalable and Efficient Port-Based Adaptive Resource Monitoring Approach in Software Defined Networks . Mehmet Akif Ersoy Üniversitesi Fen Bilimleri Enstitüsü Dergisi , 13 (1) , 9-26 . DOI: 10.29048/makufebed.1003502

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