Content-aware Traffic Engineering

Also appears as TU-Berlin technical report 2012-3, ISSN: 1436-9915Also appears as TU-Berlin technical report 2012-3, ISSN: 1436-9915Today, a large fraction of Internet traffic is originated by Content Providers (CPs) such as content distribution networks and hyper-giants. To cope with the increasing demand for content, CPs deploy massively distributed infrastructures. This poses new challenges for CPs as they have to dynamically map end-users to appropriate servers, without being fully aware of network conditions within an ISP as well as the end-users network locations. Furthermore, ISPs struggle to cope with rapid traffic shifts caused by the dynamic server selection process of CPs. In this paper, we argue that the challenges that CPs and ISPs face separately today can be turned into an opportunity. We show how they can jointly take advantage of the deployed distributed infrastructures to improve their operation and end-user performance. We propose Content-aware Traffic Engineering (CaTE), which dynamically adapts the traffic demand for content hosted on CPs by utilizing ISP network information and end-user location during the server selection process. As a result, CPs enhance their end-user to server mapping and improve end-user experience, thanks to the ability of network-informed server selection to circumvent network bottlenecks. In addition, ISPs gain the ability to partially influence the traffic demands in their networks. Our results with operational data show improvements in path length and delay between end-user and the assigned CP server, network wide traffic reduction of up to 15%, and a decrease in ISP link utilization of up to 40% when applying CaTE to traffic delivered by a small number of major CPs

Towards Runtime Verification of Programmable Switches

Is it possible to patch software bugs in P4 programs without human involvement? We show that this is partially possible in many cases due to advances in software testing and the structure of P4 programs. Our insight is that runtime verification can detect bugs, even those that are not detected at compile-time, with machine learning-guided fuzzing. This enables a more automated and real-time localization of bugs in P4 programs using software testing techniques like Tarantula. Once the bug in a P4 program is localized, the faulty code can be patched due to the programmable nature of P4. In addition, platform-dependent bugs can be detected. From P4_14 to P4_16 (latest version), our observation is that as the programmable blocks increase, the patchability of P4 programs increases accordingly. To this end, we design, develop, and evaluate P6 that (a) detects, (b) localizes, and (c) patches bugs in P4 programs with minimal human interaction. P6 tests P4 switch non-intrusively, i.e., requires no modification to the P4 program for detecting and localizing bugs. We used a P6 prototype to detect and patch seven existing bugs in eight publicly available P4 application programs deployed on two different switch platforms: behavioral model (bmv2) and Tofino. Our evaluation shows that P6 significantly outperforms bug detection baselines while generating fewer packets and patches bugs in P4 programs such as switch.p4 without triggering any regressions

A view of Internet Traffic Shifts at {ISP} and {IXPs} during the {COVID}-19 Pandemic

Due to the COVID-19 pandemic, many governments imposed lockdowns 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.EC/H2020/679158/EU/Resolving the Tussle in the Internet: Mapping, Architecture, and Policy Making/ResolutioNe

Towards a traffic map of the Internet Connecting the dots between popular services and users: Connecting the dots between popular services and users

The impact of Internet phenomena depends on how they impact users, but researchers lack visibility into how to translate Internet events into their impact. Distressingly, the research community seems to have lost hope of obtaining this information without relying on privileged viewpoints. We argue for optimism thanks to new network measurement methods and changes in Internet structure which make it possible to construct an "Internet traffic map". This map would identify the locations of users and major services, the paths between them, and the relative activity levels routed along these paths. We sketch our vision for the map, detail new measurement ideas for map construction, and identify key challenges that the research community should tackle. The realization of an Internet traffic map will be an Internet-scale research effort with Internet-scale impacts that reach far beyond the research community, and so we hope our fellow researchers are excited to join us in addressing this challenge

Back-Office Web Traffic on The Internet

Although traffic between Web servers and Web browsers is readily apparent to many knowledgeable end users, fewer are aware of the extent of server-to-server Web traffic carried over the public Internet. We refer to the former class of traffic as front-office Internet Web traffic and the latter as back-office Internet Web traffic (or just front-office and back-office traffic, for short). Back-office traffic, which may or may not be triggered by end-user activity, is essential for today's Web as it supports a number of popular but complex Web services including large-scale content delivery, social networking, indexing, searching, advertising, and proxy services. This paper takes a first look at back-office traffic, measuring it from various vantage points, including from within ISPs, IXPs, and CDNs. We describe techniques for identifying back-office traffic based on the roles that this traffic plays in the Web ecosystem. Our measurements show that back-office traffic accounts for a significant fraction not only of core Internet traffic, but also of Web transactions in the terms of requests and responses. Finally, we discuss the implications and opportunities that the presence of back-office traffic presents for the evolution of the Internet ecosystem