Proceedings of the ACM Internet Measurement Conference最新文献_第6页

The Lockdown Effect: Implications of the COVID-19 Pandemic on Internet Traffic 封锁效应:COVID-19大流行对互联网流量的影响

Proceedings of the ACM Internet Measurement Conference Pub Date : 2020-08-25 DOI: 10.1145/3419394.3423658

A. Feldmann, Oliver Gasser, F. Lichtblau, Enric Pujol-Gil, Ingmar Poese, C. Dietzel, Daniel Wagner, M. Wichtlhuber, J. Tapiador, N. Vallina-Rodriguez, O. Hohlfeld, Georgios Smaragdakis

{"title":"The Lockdown Effect: Implications of the COVID-19 Pandemic on Internet Traffic","authors":"A. Feldmann, Oliver Gasser, F. Lichtblau, Enric Pujol-Gil, Ingmar Poese, C. Dietzel, Daniel Wagner, M. Wichtlhuber, J. Tapiador, N. Vallina-Rodriguez, O. Hohlfeld, Georgios Smaragdakis","doi":"10.1145/3419394.3423658","DOIUrl":"https://doi.org/10.1145/3419394.3423658","url":null,"abstract":"Due to the COVID-19 pandemic, many governments imposed lock-downs that forced hundreds of millions of citizens to stay at home. The implementation of confinement measures increased Internet traffic demands of residential users, in particular, for remote working, entertainment, commerce, and education, which, as a result, caused traffic shifts in the Internet core. In this paper, using data from a diverse set of vantage points (one ISP, three IXPs, and one metropolitan educational network), we examine the effect of these lockdowns on traffic shifts. We find that the traffic volume increased by 15-20% almost within a week---while overall still modest, this constitutes a large increase within this short time period. However, despite this surge, we observe that the Internet infrastructure is able to handle the new volume, as most traffic shifts occur outside of traditional peak hours. When looking directly at the traffic sources, it turns out that, while hypergiants still contribute a significant fraction of traffic, we see (1) a higher increase in traffic of non-hypergiants, and (2) traffic increases in applications that people use when at home, such as Web conferencing, VPN, and gaming. While many networks see increased traffic demands, in particular, those providing services to residential users, academic networks experience major overall decreases. Yet, in these networks, we can observe substantial increases when considering applications associated to remote working and lecturing.","PeriodicalId":255324,"journal":{"name":"Proceedings of the ACM Internet Measurement Conference","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132529448","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: 163

Where Things Roam: Uncovering Cellular IoT/M2M Connectivity 万物漫游:揭示蜂窝物联网/M2M连接

Proceedings of the ACM Internet Measurement Conference Pub Date : 2020-07-27 DOI: 10.1145/3419394.3423661

Andra Lutu, Byunjin Jun, A. Finamore, F. Bustamante, Diego Perino

{"title":"Where Things Roam: Uncovering Cellular IoT/M2M Connectivity","authors":"Andra Lutu, Byunjin Jun, A. Finamore, F. Bustamante, Diego Perino","doi":"10.1145/3419394.3423661","DOIUrl":"https://doi.org/10.1145/3419394.3423661","url":null,"abstract":"Support for \"things\" roaming internationally has become critical for Internet of Things (IoT) verticals, from connected cars to smart meters and wearables, and explains the commercial success of Machine-to-Machine (M2M) platforms. We analyze IoT verticals operating with connectivity via IoT SIMs, and present the first large-scale study of commercially deployed IoT SIMs for energy meters. We also present the first characterization of an operational M2M platform and the first analysis of the rather opaque associated ecosystem. For operators, the exponential growth of IoT has meant increased stress on the infrastructure shared with traditional roaming traffic. Our analysis quantifies the adoption of roaming by M2M platforms and the impact they have on the underlying visited Mobile Network Operators (MNOs). To manage the impact of massive deployments of device operating with an IoT SIM, operators must be able to distinguish between the latter and traditional inbound roamers. We build a comprehensive dataset capturing the device population of a large European MNO over three weeks. With this, we propose and validate a classification approach that can allow operators to distinguish inbound roaming IoT devices.","PeriodicalId":255324,"journal":{"name":"Proceedings of the ACM Internet Measurement Conference","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133311366","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: 9

Revisiting Transactional Statistics of High-scalability Blockchains 重新审视高可扩展性区块链的事务统计

Proceedings of the ACM Internet Measurement Conference Pub Date : 2020-03-05 DOI: 10.1145/3419394.3423628

Daniel Perez, Jiahua Xu, B. Livshits

引用次数: 21

Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions 使用gan共享网络时间序列数据:挑战，最初的承诺和开放的问题

Proceedings of the ACM Internet Measurement Conference Pub Date : 2019-09-30 DOI: 10.1145/3419394.3423643

Zinan Lin, Alankar Jain, Chen Wang, G. Fanti, V. Sekar

{"title":"Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions","authors":"Zinan Lin, Alankar Jain, Chen Wang, G. Fanti, V. Sekar","doi":"10.1145/3419394.3423643","DOIUrl":"https://doi.org/10.1145/3419394.3423643","url":null,"abstract":"Limited data access is a longstanding barrier to data-driven research and development in the networked systems community. In this work, we explore if and how generative adversarial networks (GANs) can be used to incentivize data sharing by enabling a generic framework for sharing synthetic datasets with minimal expert knowledge. As a specific target, our focus in this paper is on time series datasets with metadata (e.g., packet loss rate measurements with corresponding ISPs). We identify key challenges of existing GAN approaches for such workloads with respect to fidelity (e.g., long-term dependencies, complex multidimensional relationships, mode collapse) and privacy (i.e., existing guarantees are poorly understood and can sacrifice fidelity). To improve fidelity, we design a custom workflow called DoppelGANger (DG) and demonstrate that across diverse real-world datasets (e.g., bandwidth measurements, cluster requests, web sessions) and use cases (e.g., structural characterization, predictive modeling, algorithm comparison), DG achieves up to 43% better fidelity than baseline models. Although we do not resolve the privacy problem in this work, we identify fundamental challenges with both classical notions of privacy and recent advances to improve the privacy properties of GANs, and suggest a potential roadmap for addressing these challenges. By shedding light on the promise and challenges, we hope our work can rekindle the conversation on workflows for data sharing.","PeriodicalId":255324,"journal":{"name":"Proceedings of the ACM Internet Measurement Conference","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114234521","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: 81