Available bandwidth estimation metrics as tools to evaluate network trunk links
Nowadays the platform par excellence for the development of all telecommunication activities is the Internet; and its infrastructure is facing new challenges every day due to the growth in demand for more content, such as streaming video, storage, and cloud processing. Also, to maintain optimal leve...
- Autores:
-
Salcedo, Dixon
Cabajal, Angel
Gutierrez, Gilberto Eduardo
Castro, Oscar
Esmeral, Ernesto
Urueta, Jesús
Rico, Miguel
Henriquez, Carlos
Suarez, Diana
Mardini, Johan
Ortíz, Daniel
Bernal, Daniel
Cortes, Albeiro
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7810
- Acceso en línea:
- https://hdl.handle.net/11323/7810
https://repositorio.cuc.edu.co/
- Palabra clave:
- Available bandwidth
Network trunk links
Heterogeneous networks
Quality of service
- Rights
- openAccess
- License
- CC0 1.0 Universal
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| dc.title.spa.fl_str_mv |
Available bandwidth estimation metrics as tools to evaluate network trunk links |
| title |
Available bandwidth estimation metrics as tools to evaluate network trunk links |
| spellingShingle |
Available bandwidth estimation metrics as tools to evaluate network trunk links Available bandwidth Network trunk links Heterogeneous networks Quality of service |
| title_short |
Available bandwidth estimation metrics as tools to evaluate network trunk links |
| title_full |
Available bandwidth estimation metrics as tools to evaluate network trunk links |
| title_fullStr |
Available bandwidth estimation metrics as tools to evaluate network trunk links |
| title_full_unstemmed |
Available bandwidth estimation metrics as tools to evaluate network trunk links |
| title_sort |
Available bandwidth estimation metrics as tools to evaluate network trunk links |
| dc.creator.fl_str_mv |
Salcedo, Dixon Cabajal, Angel Gutierrez, Gilberto Eduardo Castro, Oscar Esmeral, Ernesto Urueta, Jesús Rico, Miguel Henriquez, Carlos Suarez, Diana Mardini, Johan Ortíz, Daniel Bernal, Daniel Cortes, Albeiro |
| dc.contributor.author.spa.fl_str_mv |
Salcedo, Dixon Cabajal, Angel Gutierrez, Gilberto Eduardo Castro, Oscar Esmeral, Ernesto Urueta, Jesús Rico, Miguel Henriquez, Carlos Suarez, Diana Mardini, Johan Ortíz, Daniel Bernal, Daniel Cortes, Albeiro |
| dc.subject.spa.fl_str_mv |
Available bandwidth Network trunk links Heterogeneous networks Quality of service |
| topic |
Available bandwidth Network trunk links Heterogeneous networks Quality of service |
| description |
Nowadays the platform par excellence for the development of all telecommunication activities is the Internet; and its infrastructure is facing new challenges every day due to the growth in demand for more content, such as streaming video, storage, and cloud processing. Also, to maintain optimal levels of service quality, network applications demand more telecommunication resources. Similarly, the network infrastructures that support these applications have evolved, and demand greater and more efficient management of the trunk links, which play a primary role in sustaining services. Therefore, this paper presents the performance evaluation of trunk, wired and wireless links in a heterogeneous computer network infrastructure, using available bandwidth estimation tools such as IGI, Pathload, and Traceband. Thus, for the experimental evaluation of the trunk links, two real network scenarios were implemented, where crosstraffic was generated in a synthetic way using the Mgen tool. Consequently, this study allowed verifying in other aspects; that the metrics of the estimation tools can be used to evaluate and know the performance of wired and wireless trunk links, which can be reliable up to 96% for network administrative tasks. |
| publishDate |
2020 |
| dc.date.issued.none.fl_str_mv |
2020-11 |
| dc.date.accessioned.none.fl_str_mv |
2021-02-02T20:26:26Z |
| dc.date.available.none.fl_str_mv |
2021-02-02T20:26:26Z |
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Artículo de revista |
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https://hdl.handle.net/11323/7810 |
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Corporación Universidad de la Costa |
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eng |
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eng |
| dc.relation.references.spa.fl_str_mv |
A. Tirumala J. D. & Gibbs K. 2006. Iperf. Iperf. Abolfazli S., Sanaei Z., Wong S. Y., Tabassi A. & Rosen S. 2015. Throughput measurement in 4G wireless data networks: Performance evaluation and validation. Computer Applications & Industrial Electronics (ISCAIE), 2015 IEEE Symposium on, (pp. 27-32). doi:10.1109/ISCAIE.2015.7298322 Aceto G., Palumbo F., Persico V., Chen H. & Pescape A. 2018. Evaluation of SDN-based bandwidth estimation in Mobile Broad Band networks. 2018 24th Asia-Pacific Conference on Communications (APCC), (pp. 263-268). Retrieved from https://ieeexplore.ieee.org/abstract/document/8633562 Adamson B. & Gallavan S. 1997. Multi-Generator. MultiGenerator. Azevedo D., Bonfim M., Lima L. & Fernandes S. 2018. Towards an Accurate Bandwidth Estimation Tool for 802.11 n Wireless Networks. 2018 IEEE Symposium on Computers and Communications (ISCC), (pp. 00486– 00491). doi:10.1109/ISCC.2018.8538751 Botta A., Davy A., Meskill B. & Aceto G. 2013. Active Techniques for Available Bandwidth Estimation: Comparison and Application. In E. Biersack, C. Callegari, & M. Matijasevic (Eds.), Data Traffic Monitoring and Analysis (7754: 28-43). Springer Berlin Heidelberg. doi:10.1007/978-3-642-36784-7_2 Chan K. 2015. Testing and Measurement: Techniques and Applications: Proceedings of the 2015 International Conference on Testing and Measurement Techniques (TMTA 2015), 16-17 January 2015, Phuket Island, Thailand. CRC Press. Dely Kassler A., Chow L., Bambos N., Bayer N., Einsiedler H. & PeyloC. 2014. BEST-AP: Non-intrusive Estimation of Available Bandwidth and its Application for Dynamic Access Point selection. Comuputer Comunicactions. 78-91. Guerrero C. D. & Morillo D. S. 2012. On the reduction of the available bandwidth estimation error through clustering with k-means. 2012 IEEE Latin-America Conference on Communications, (pp. 1-5). doi:10.1109/LATINCOM.2012.6506020 Guerrero C. & Labrador. M. 2010. On the Applicability of Available Bandwidth Estimation Techniques and Tools. Computer Communications, 33, 11-22. doi:http://dx.doi.org/10.1016/j.comcom.2009.08.010 Guerrero C. & Labrador. M. 2010. Traceband: A Fast, Low Overhead and Accurate Tool for Available Bandwidth Estimation and Monitoring. Computer Networks, 54, 977-990. doi:http://dx.doi.org/10.1016/j.comnet.2009.09.024 Guerrero C., Salcedo D. & Lamos H. 2013, 5. A Clustering Approach to Reduce the Available Bandwidth Estimation Error. Latin America Transactions, IEEE (Revista IEEE America Latina), 11, 927-932. doi:10.1109/TLA.2013.6568835 Ha P. & Xu L. 2018, 4. Available bandwidth estimation in public clouds. IEEE INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (INFOCOMWKSHPS), (pp. 238-243). doi:10.1109/INFCOMW.2018.8407010 Hernandez I. & Insuasty D. 2015. Protocol Approach to Test AVBW Tools in Wireless Environment. VII Congreso Iberoamericano de Telemática CITA2015, 7, 4. Retrieved from http://www.researchandinnovationbook.com/PROCEEDINGS/CITA2015/Archives/papers/paper51.pdf Jain M. & Dovrolis C. 2004. Ten Fallacies and Pitfalls on End-to-end Available Bandwidth Estimation. Proceedings of the 4th ACM SIGCOMM Conference on Internet Measurement (pp. 272-277). New York, NY, USA: ACM. doi:10.1145/1028788.1028825 Jain M. & Dovrolis C. 2008, 8. Path Selection Using Available Bandwidth Estimation in Overlay-based Video Streaming. Computer Network., 52, 2411-2418. doi:10.1016/j.comnet.2008.04.019 Jain M. & Dovrolis. C. 2002. Pathload: A Measurement Tool for End-to-End Available Bandwidth. In Proceedings of Passive and Active Measurements (PAM) Workshop, (pp. 14-25). doi:10.1.1.17.4495 Joshi K. & Benson T. 2016, 11. Network Function Virtualization. IEEE Internet Computing, 20, 7-9. doi:10.1109/MIC.2016.112 Kapoor R., Lao C., Chen L., Gerla & Sanadidi Y. 2004, 8. CapProbe: A Simple and Accurate Capacity Estimation Technique. ACM SIGCOMM Computer Communication Review. 34, pp. 67-78. doi:10.1145/1015467.1015476 Kirova V., Siemens E., Kachan D., Vasylenko O. & Karpov K. 2018. Optimization of Probe Train Size for Available Bandwidth Estimation in High-speed Networks. MATEC Web of Conferences. 208, p. 02001. Nguyen U., Tran D. & Nguyen G. 2014. A Taxonomy of Applying Filter Techniques to Improve the Available Bandwidth Estimations. Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication (pp. 18:1-18:8). New York, NY, USA: ACM. doi:10.1145/2557977.2558004 Paul A. K., Tachibana A. & Hasegawa T. 2016. NEXTFIT: Available Bandwidth Measurement over 4G/LTE Networks-A Curve-Fitting Approach. 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), (pp. 25-32). doi:10.1109/AINA.2016.24 Salcedo D., Guerrero C. D. & Martinez R. 2018. Available Bandwidth Estimation Tools: Metrics, Approach and Performance. International Journal of Communication Networks and Information Security, 10, 580. Retrieved from http://www.ijcnis.org/index.php/ijcnis/article/view/3516/324 Salcedo D., Guerrero C. & Guérrero J. 2017, 12. Overhead in Available Bandwidth Estimation Tools: Evaluation and Analysis. International Journal of Communication Networks and Information Security (IJCNIS), 9, 393-402. Retrieved from http://www.ijcnis.org/index.php/ijcnis/article/view/2475 Shi J., Yang Q., Gou G. & Xiong G. 2020. An Quick Available-Bandwidth Measurement Method Based on Link Delay Growth Rate. Proceedings of the 2020 8th International Conference on Communications and Broadband Networking. 51-57. Shiobara S. & Okamawari T. 2017. A Novel Available Bandwidth Estimation Method for Mobile Networks Using a Train of Packet Groups. Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication (pp. 59: 1-59: 7). New York, NY, USA: ACM. doi:10.1145/3022227.3022285 Weforum. 2020, MArzo. Worl Economic Forum. Retrieved from https://www.weforum.org/ |
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Salcedo, DixonCabajal, AngelGutierrez, Gilberto EduardoCastro, OscarEsmeral, ErnestoUrueta, JesúsRico, MiguelHenriquez, CarlosSuarez, DianaMardini, JohanOrtíz, DanielBernal, DanielCortes, Albeiro2021-02-02T20:26:26Z2021-02-02T20:26:26Z2020-111819-6608https://hdl.handle.net/11323/7810Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Nowadays the platform par excellence for the development of all telecommunication activities is the Internet; and its infrastructure is facing new challenges every day due to the growth in demand for more content, such as streaming video, storage, and cloud processing. Also, to maintain optimal levels of service quality, network applications demand more telecommunication resources. Similarly, the network infrastructures that support these applications have evolved, and demand greater and more efficient management of the trunk links, which play a primary role in sustaining services. Therefore, this paper presents the performance evaluation of trunk, wired and wireless links in a heterogeneous computer network infrastructure, using available bandwidth estimation tools such as IGI, Pathload, and Traceband. Thus, for the experimental evaluation of the trunk links, two real network scenarios were implemented, where crosstraffic was generated in a synthetic way using the Mgen tool. Consequently, this study allowed verifying in other aspects; that the metrics of the estimation tools can be used to evaluate and know the performance of wired and wireless trunk links, which can be reliable up to 96% for network administrative tasks.Salcedo, DixonCabajal, AngelGutierrez, Eduardo-will be generated-orcid-0000-0002-5116-676X-600Castro, OscarEsmeral, Ernesto-will be generated-orcid-0000-0002-7526-8349-600Urueta, JesúsRico, MiguelHenriquez, CarlosSuarez, DianaMardini, JohanOrtíz, DanielBernal, Daniel-will be generated-orcid-0000-0003-4164-3814-600Cortes, Albeiroapplication/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2ARPN Journal of Engineering and Applied Scienceshttps://www.researchgate.net/publication/348845555_AVAILABLE_BANDWIDTH_ESTIMATION_METRICS_AS_TOOLS_TO_EVALUATE_NETWORK_TRUNK_LINKSAvailable bandwidthNetwork trunk linksHeterogeneous networksQuality of serviceAvailable bandwidth estimation metrics as tools to evaluate network trunk linksArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionA. Tirumala J. D. & Gibbs K. 2006. Iperf. Iperf.Abolfazli S., Sanaei Z., Wong S. Y., Tabassi A. & Rosen S. 2015. Throughput measurement in 4G wireless data networks: Performance evaluation and validation. Computer Applications & Industrial Electronics (ISCAIE), 2015 IEEE Symposium on, (pp. 27-32). doi:10.1109/ISCAIE.2015.7298322Aceto G., Palumbo F., Persico V., Chen H. & Pescape A. 2018. Evaluation of SDN-based bandwidth estimation in Mobile Broad Band networks. 2018 24th Asia-Pacific Conference on Communications (APCC), (pp. 263-268). Retrieved from https://ieeexplore.ieee.org/abstract/document/8633562Adamson B. & Gallavan S. 1997. Multi-Generator. MultiGenerator.Azevedo D., Bonfim M., Lima L. & Fernandes S. 2018. Towards an Accurate Bandwidth Estimation Tool for 802.11 n Wireless Networks. 2018 IEEE Symposium on Computers and Communications (ISCC), (pp. 00486– 00491). doi:10.1109/ISCC.2018.8538751Botta A., Davy A., Meskill B. & Aceto G. 2013. Active Techniques for Available Bandwidth Estimation: Comparison and Application. In E. Biersack, C. Callegari, & M. Matijasevic (Eds.), Data Traffic Monitoring and Analysis (7754: 28-43). Springer Berlin Heidelberg. doi:10.1007/978-3-642-36784-7_2Chan K. 2015. Testing and Measurement: Techniques and Applications: Proceedings of the 2015 International Conference on Testing and Measurement Techniques (TMTA 2015), 16-17 January 2015, Phuket Island, Thailand. CRC Press.Dely Kassler A., Chow L., Bambos N., Bayer N., Einsiedler H. & PeyloC. 2014. BEST-AP: Non-intrusive Estimation of Available Bandwidth and its Application for Dynamic Access Point selection. Comuputer Comunicactions. 78-91.Guerrero C. D. & Morillo D. S. 2012. On the reduction of the available bandwidth estimation error through clustering with k-means. 2012 IEEE Latin-America Conference on Communications, (pp. 1-5). doi:10.1109/LATINCOM.2012.6506020Guerrero C. & Labrador. M. 2010. On the Applicability of Available Bandwidth Estimation Techniques and Tools. Computer Communications, 33, 11-22. doi:http://dx.doi.org/10.1016/j.comcom.2009.08.010Guerrero C. & Labrador. M. 2010. Traceband: A Fast, Low Overhead and Accurate Tool for Available Bandwidth Estimation and Monitoring. Computer Networks, 54, 977-990. doi:http://dx.doi.org/10.1016/j.comnet.2009.09.024Guerrero C., Salcedo D. & Lamos H. 2013, 5. A Clustering Approach to Reduce the Available Bandwidth Estimation Error. Latin America Transactions, IEEE (Revista IEEE America Latina), 11, 927-932. doi:10.1109/TLA.2013.6568835Ha P. & Xu L. 2018, 4. Available bandwidth estimation in public clouds. IEEE INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (INFOCOMWKSHPS), (pp. 238-243). doi:10.1109/INFCOMW.2018.8407010Hernandez I. & Insuasty D. 2015. Protocol Approach to Test AVBW Tools in Wireless Environment. VII Congreso Iberoamericano de Telemática CITA2015, 7, 4. Retrieved from http://www.researchandinnovationbook.com/PROCEEDINGS/CITA2015/Archives/papers/paper51.pdfJain M. & Dovrolis C. 2004. Ten Fallacies and Pitfalls on End-to-end Available Bandwidth Estimation. Proceedings of the 4th ACM SIGCOMM Conference on Internet Measurement (pp. 272-277). New York, NY, USA: ACM. doi:10.1145/1028788.1028825Jain M. & Dovrolis C. 2008, 8. Path Selection Using Available Bandwidth Estimation in Overlay-based Video Streaming. Computer Network., 52, 2411-2418. doi:10.1016/j.comnet.2008.04.019Jain M. & Dovrolis. C. 2002. Pathload: A Measurement Tool for End-to-End Available Bandwidth. In Proceedings of Passive and Active Measurements (PAM) Workshop, (pp. 14-25). doi:10.1.1.17.4495Joshi K. & Benson T. 2016, 11. Network Function Virtualization. IEEE Internet Computing, 20, 7-9. doi:10.1109/MIC.2016.112Kapoor R., Lao C., Chen L., Gerla & Sanadidi Y. 2004, 8. CapProbe: A Simple and Accurate Capacity Estimation Technique. ACM SIGCOMM Computer Communication Review. 34, pp. 67-78. doi:10.1145/1015467.1015476Kirova V., Siemens E., Kachan D., Vasylenko O. & Karpov K. 2018. Optimization of Probe Train Size for Available Bandwidth Estimation in High-speed Networks. MATEC Web of Conferences. 208, p. 02001.Nguyen U., Tran D. & Nguyen G. 2014. A Taxonomy of Applying Filter Techniques to Improve the Available Bandwidth Estimations. Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication (pp. 18:1-18:8). New York, NY, USA: ACM. doi:10.1145/2557977.2558004Paul A. K., Tachibana A. & Hasegawa T. 2016. NEXTFIT: Available Bandwidth Measurement over 4G/LTE Networks-A Curve-Fitting Approach. 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), (pp. 25-32). doi:10.1109/AINA.2016.24Salcedo D., Guerrero C. D. & Martinez R. 2018. Available Bandwidth Estimation Tools: Metrics, Approach and Performance. International Journal of Communication Networks and Information Security, 10, 580. Retrieved from http://www.ijcnis.org/index.php/ijcnis/article/view/3516/324Salcedo D., Guerrero C. & Guérrero J. 2017, 12. Overhead in Available Bandwidth Estimation Tools: Evaluation and Analysis. International Journal of Communication Networks and Information Security (IJCNIS), 9, 393-402. Retrieved from http://www.ijcnis.org/index.php/ijcnis/article/view/2475Shi J., Yang Q., Gou G. & Xiong G. 2020. An Quick Available-Bandwidth Measurement Method Based on Link Delay Growth Rate. Proceedings of the 2020 8th International Conference on Communications and Broadband Networking. 51-57.Shiobara S. & Okamawari T. 2017. A Novel Available Bandwidth Estimation Method for Mobile Networks Using a Train of Packet Groups. Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication (pp. 59: 1-59: 7). New York, NY, USA: ACM. doi:10.1145/3022227.3022285Weforum. 2020, MArzo. Worl Economic Forum. 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