2021 IEEE Workshop on Innovating the Network for Data-Intensive Science (INDIS)最新文献

The Service Analysis and Network Diagnosis Data Pipeline 业务分析与网络诊断数据管道

2021 IEEE Workshop on Innovating the Network for Data-Intensive Science (INDIS) Pub Date : 2021-11-01 DOI: 10.1109/indis54524.2021.00006

D. Weitzel, Shawn McKee, B. Bockelman, J. Thiltges, M. Babik, I. Vukotic

引用次数: 0

NetGraf: An End-to-End Learning Network Monitoring Service NetGraf:端到端学习网络监控服务

2021 IEEE Workshop on Innovating the Network for Data-Intensive Science (INDIS) Pub Date : 2021-11-01 DOI: 10.1109/indis54524.2021.00007

Bashir Mohammed, M. Kiran, B. Enders

{"title":"NetGraf: An End-to-End Learning Network Monitoring Service","authors":"Bashir Mohammed, M. Kiran, B. Enders","doi":"10.1109/indis54524.2021.00007","DOIUrl":"https://doi.org/10.1109/indis54524.2021.00007","url":null,"abstract":"Network monitoring services are of enormous importance to ensure optimal performance is being delivered and help determine any failing services. Particularly for large data transfers, checking key performance indicators like throughput, packet loss, and latency can make or break experiment results. However, network monitoring tools are very diverse in metrics collected and dependent on the devices installed. Additionally, there are limited tools that can learn and determine the cause of degraded performance. This paper presents NetGraf, a novel end-to-end learning monitoring system that utilizes current monitoring tools, merges multiple data sources into one dashboard for easy use, and provides machine learning libraries to analyze the data and perform real-time anomaly finding. Using a database backend, NetGraf can learn performance trends and show users if network performance has degraded. We demonstrate how NetGraf can easily be deployed through automation services and linked to multiple monitoring sources to collect data. Via the machine learning innovation and merging various data sources, NetGraf aims to fulfill the need for holistic learning network telemetry monitoring. To the best of our knowledge, this is the first-ever end-to-end learning monitoring service. We demonstrate its use on two network setups to showcase its impact.","PeriodicalId":351712,"journal":{"name":"2021 IEEE Workshop on Innovating the Network for Data-Intensive Science (INDIS)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132888797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Learning Transfers via Transfer Learning 通过迁移学习学习迁移

2021 IEEE Workshop on Innovating the Network for Data-Intensive Science (INDIS) Pub Date : 2021-11-01 DOI: 10.1109/indis54524.2021.00009

Md. Arifuzzaman, Engin Arslan

{"title":"Learning Transfers via Transfer Learning","authors":"Md. Arifuzzaman, Engin Arslan","doi":"10.1109/indis54524.2021.00009","DOIUrl":"https://doi.org/10.1109/indis54524.2021.00009","url":null,"abstract":"Detecting performance anomalies is key to efficiently utilize network resources and improve the quality of service. Researchers proposed various approaches to identify the presence of anomalies by analyzing performance statistics using heuristic (e.g., change point detection) and Machine Learning (ML) models. Although these models yield high accuracy in the networks that they are trained for, their performance degrade severely when transferred to different network settings. This is because of the fact that existing models detect anomalies by capturing the changes in transfer throughput and observed RTT values, which are dependent to network settings. In this paper, we propose a novel feature transformation method to eliminate network dependence of ML models for anomaly diagnosis problems to enhance their performance when transferred to new networks (aka transfer learning) thereby mitigating the need to gather training data in each network separately. We validate the findings through experimental evaluations conducted on simulated and production networks and show that the proposed feature transformation improves the performance of transfer learning for anomaly diagnosis problems from less than 60% to over 90%. Finally, we evaluate the performance of the proposed solutions using various congestion control algorithm and observe that the models trained using BBR attains the best transfer learning performance compared to Cubic and HTCP.","PeriodicalId":351712,"journal":{"name":"2021 IEEE Workshop on Innovating the Network for Data-Intensive Science (INDIS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129980350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Exploring the BBRv2 Congestion Control Algorithm for use on Data Transfer Nodes 探讨用于数据传输节点的BBRv2拥塞控制算法

2021 IEEE Workshop on Innovating the Network for Data-Intensive Science (INDIS) Pub Date : 2021-11-01 DOI: 10.1109/indis54524.2021.00008

Brendan Tierney, E. Dart, E. Kissel

引用次数: 4

Bridging Network and Parallel I/O Research for Improving Data-Intensive Distributed Applications 桥接网络与并行I/O改进数据密集型分布式应用的研究

2021 IEEE Workshop on Innovating the Network for Data-Intensive Science (INDIS) Pub Date : 2021-11-01 DOI: 10.1109/indis54524.2021.00011

Debasmita Biswas, Sarah Neuwirth, A. Paul, A. Butt

引用次数: 2

Deploying per-packet telemetry in a long-haul network: the AmLight use case 在长途网络中部署逐包遥测:AmLight用例

2021 IEEE Workshop on Innovating the Network for Data-Intensive Science (INDIS) Pub Date : 2021-11-01 DOI: 10.1109/indis54524.2021.00010

Jeronimo Bezerra, I. Brito, A. Quintana, J. Ibarra, V. Chergarova, Renata Frez, Heidi Morgan, Marc LeClerc, Arun Paneri

{"title":"Deploying per-packet telemetry in a long-haul network: the AmLight use case","authors":"Jeronimo Bezerra, I. Brito, A. Quintana, J. Ibarra, V. Chergarova, Renata Frez, Heidi Morgan, Marc LeClerc, Arun Paneri","doi":"10.1109/indis54524.2021.00010","DOIUrl":"https://doi.org/10.1109/indis54524.2021.00010","url":null,"abstract":"Long-haul networks are growing in complexity to address the constant need for more bandwidth, lower latency and jitter, customized traffic prioritization, and SLA-grade network resilience. A more complex infrastructure requires a deeper visibility of the assets to optimize the resource utilization as well as to protect the infrastructure and users connected to it. Leveraging legacy network monitoring technologies, such as SNMP, is not enough, since they do not offer real-time and granular visibility. That's where per-packet monitoring solutions can become a game changer. In-band Network Telemetry (INT) offers per-packet visibility with no impact to the network's forwarding plane. Adding per-packet visibility has the potential to change the network monitoring and operations field, and to redefine how traffic engineering will take place in the future. This paper aims to showcase how INT can dramatically increase network visibility, down to a sub-second scale. Experiments and findings come from using the AmLight long-haul academic network as a use case.","PeriodicalId":351712,"journal":{"name":"2021 IEEE Workshop on Innovating the Network for Data-Intensive Science (INDIS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132075850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

[Copyright notice] (版权)

2021 IEEE Workshop on Innovating the Network for Data-Intensive Science (INDIS) Pub Date : 2021-11-01 DOI: 10.1109/indis54524.2021.00002

引用次数: 0

[Title page] (标题页)

2021 IEEE Workshop on Innovating the Network for Data-Intensive Science (INDIS) Pub Date : 2021-11-01 DOI: 10.1109/indis54524.2021.00001

引用次数: 0