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Clearing Clouds from the Horizon: Latency Characterization of Public Cloud Service Platforms
| dc.contributor.author | Ingabire, Rita | |
| dc.contributor.author | Bazco-Nogueras, Antonio | |
| dc.contributor.author | Mancuso, Vincenzo | |
| dc.contributor.author | Contreras, Luis M. | |
| dc.contributor.author | Folgueira, Jesus | |
| dc.date.accessioned | 2024-07-15T13:12:36Z | |
| dc.date.available | 2024-07-15T13:12:36Z | |
| dc.date.issued | 2024-07-29 | |
| dc.identifier.citation | [1] T. Koch, W. Jiang, T. Luo et al., “Towards a traffic map of the internet: Connecting the dots between popular services and users,” in Proc. ACM Workshop on Hot Topics in Networks (HotNets). ACM, 2021, p. 23–30. [2] P. Casas and R. Schatz, “Quality of Experience in Cloud services: Survey and measurements,” Computer Networks, vol. 68, pp. 149–165, 2014. [3] A. Li, X. Yang, S. Kandula, and M. Zhang, “Cloudcmp: Comparing public cloud providers,” in Proc. ACM Internet Measurement Conf. (ACM IMC). ACM, 2010, p. 1–14. [4] F. Palumbo, G. Aceto, A. Botta et al., “Characterization and analysis of cloud-to-user latency: The case of Azure and AWS,” Computer Networks, vol. 184, p. 107693, 2021. [5] N. Mohan, L. Corneo, A. Zavodovski et al., “Pruning edge research with latency shears,” in Proc. ACM Workshop on Hot Topics in Networks (HotNets). ACM, 2020, p. 182–189. [6] F. Michelinakis, H. Doroud, A. Razaghpanah et al., “The cloud that runs the mobile internet: A measurement study of mobile cloud services,” in IEEE Conf. Computer Commun. (INFOCOM), 2018, pp. 1619–1627. [7] Y. Jin, S. Renganathan, G. Ananthanarayanan et al., “Zooming in on wide-area latencies to a global cloud provider,” in Proc. ACM SIGCOMM Conf. ACM, 2019, p. 104–116. [8] “RIPE Atlas,” https://atlas.ripe.net, Accessed April 2024. [9] Y. A. Wang, C. Huang, J. Li, and K. W. Ross, “Estimating the performance of hypothetical cloud service deployments: A measurement- based approach,” in IEEE Conf. Computer Commun. (INFOCOM), 2011, pp. 2372–2380. [10] O. Tomanek and L. Kencl, “CLAudit: Planetary-scale cloud latency au- diting platform,” in Proc. IEEE Int. Conf. Cloud Networking (CloudNet), 2013, pp. 138–146. [11] “Planetlab,” https://planetlab.cs.princeton.edu, [accessed April 2024]. [12] O. Tomanek, P. Mulinka, and L. Kencl, “Multidimensional cloud latency monitoring and evaluation,” Computer Networks, vol. 107, Oct. 2016. [13] R. K. P. Mok, H. Zou, R. Yang et al., “Measuring the network performance of google cloud platform,” Proc. ACM Internet Measurement Conf. (ACM IMC), 2021. [14] O. Alay, A. Lutu, R. García et al., “Measuring and assessing mobile broadband networks with MONROE,” in IEEE Int. Symp. on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2016. [15] R. Fontugne, A. Shah, and K. Cho, “Persistent last-mile congestion: Not so uncommon,” in Proc. ACM Internet Measurement Conf. (ACM IMC). ACM, 2020, p. 420–427. [16] L. Corneo, N. Mohan, A. Zavodovski et al., “(how much) can edge computing change network latency?” in IFIP Networking Conf., 2021. [17] M. Candela, E. Gregori, V. Luconi, and A. Vecchio, “Using RIPE Atlas for geolocating IP infrastructure,” IEEE Access, vol. 7, 2019. [18] O. Victor Babasanmi and J. Chavula, “Measuring cloud latency in Africa,” in Proc. IEEE Int. Conf. Cloud Networking (CloudNet), 2022, pp. 61–66. [19] A. Kedia, A. Ganesh, and A. Aggarwal, “Examining lower latency routing with overlay networks,” 2023, arxiv preprint: 2306.15174. [20] L. Corneo, M. Eder, N. Mohan et al., “Surrounded by the clouds: A comprehensive cloud reachability study,” in Proc. Int. World Wide Web Conf. (WWW). ACM, 2021, p. 295–304. [21] V. Bajpai, S. J. Eravuchira, and J. Schönwälder, “Lessons learned from using the RIPE Atlas platform for measurement research,” ACN SIGCOMM Comput. Commun. Rev., vol. 45, no. 3, p. 35–42, jul 2015. [22] P. Sermpezis, L. Prehn, S. Kostoglou et al., “Bias in internet measurement platforms,” in Netw. Traffic Meas. and Analysis Conf. (TMA), 2023. [23] “RIPE NCC,” https://www.ripe.net/, online; accessed 16-April-2024. [24] RIPE Atlas, “Coverage and statistics,” https://atlas.ripe.net/coverage/, 2024, [Online; accessed 01-May-2024]. [25] E. A. Petros Gigis, Vasileios Kotronis, “RIPE Atlas population cover- age,” https://sg-pub.ripe.net/petros/population_coverage/, 2024, [Online; accessed 01-May-2024]. [26] R. Kistel, “RIPE Atlas architecture - how we manage our probes,” https://labs.ripe.net/author/kistel/ripe-atlas-architecture-how-we- manage-our-probes/, 2023, online; accessed 01-May-2024. [27] A. F. Zanella, A. Bazco-Nogueras, C. Ziemlicki, and M. Fiore, “Character- izing and modeling session-level mobile traffic demands from large-scale measurements,” in Proc. ACM Internet Measurement Conf. (ACM IMC), 2023, p. 696–709. [28] C. Rudin, C. Chen, Z. Chen et al., “Interpretable machine learning: Fundamental principles and 10 grand challenges,” Statistics Surveys, vol. 16, pp. 1–85, 2022. [29] M. T. Ribeiro, S. Singh, and C. Guestrin, “Why should I trust you?: Explaining the predictions of any classifier,” in Proc. ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2016, pp. 1135–1144. [30] S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” Advances in Neural Inf. Processing Systems, vol. 30, 2017. [31] C. Elsen, “RIPE Atlas probes in AWS,” https://github.com/chriselsen/ RIPE-Atlas-in-AWSs, [Online; accessed 15-Jan-2024]. | es |
| dc.identifier.uri | https://hdl.handle.net/20.500.12761/1826 | |
| dc.description.abstract | Services rely more and more on cloud platforms to offer their products to end users. This implies that being able to estimate the latency required to reach those cloud platforms is of growing importance. To shed light on this crucial aspect, we perform a three-month measurement campaign involving traceroute measurements every 30 minutes over 256 pairs of source-destination probes, where the vantage points are located in different Cloud Service Providers (CSPs) and the destination probes belong to one of the main Infrastructure Operators (IO s) of Spain. We provide interesting insights obtained from analyzing the data resulting from this campaign. Among them, we observe that, as expected, distance is the unavoidable factor impacting cloud latency. Yet, other results are less anticipated, such as the great stability of the network, or the lack of performance difference when comparing standard and premium network service tiers. We also analyze the potential of forecasting the cloud latency both for future samples but also for unobserved connections. | es |
| dc.description.sponsorship | Spanish Ministry of Economic Affairs and Digital Transformation | es |
| dc.description.sponsorship | European Union NextGeneration-EU | es |
| dc.language.iso | eng | es |
| dc.title | Clearing Clouds from the Horizon: Latency Characterization of Public Cloud Service Platforms | es |
| dc.type | conference object | es |
| dc.conference.date | 29 - 31 July 2024 | es |
| dc.conference.place | Big Island, Hawaii, USA | es |
| dc.conference.title | International Conference on Computer Communications and Networks | * |
| dc.event.type | conference | es |
| dc.pres.type | invitedpaper | es |
| dc.type.hasVersion | AM | es |
| dc.rights.accessRights | open access | es |
| dc.page.final | 9 | es |
| dc.page.initial | 1 | es |
| dc.relation.projectID | TSI-063000-2021-38 | es |
| dc.relation.projectID | 2020-T2/TIC-20710 | es |
| dc.relation.projectName | grant 2020-T2/TIC-20710 for Talent Attraction | es |
| dc.relation.projectName | AEON-CPS Network Intelligence for cyber-physical system support | es |
| dc.subject.keyword | latency, measurements, RIPE Atlas, forecasting, cloud services, cloud performance | es |
| dc.description.refereed | TRUE | es |
| dc.description.status | inpress | es |
