In recent years, the progress in both hardware and software allows user-space applications to capture packets at 10 Gb/s line rate or more, with cheap COTS hardware. However, processing packets at such rates with software is still far from being trivial. In the literature, this challenge has been extensively studied for network intrusion detection systems, where per-packet operations are easy to parallelize with support of hardware acceleration. Conversely, the scalability of statistical traffic analyzers (STAs) is intrinsically complicated by the need to track per-flow state to collect statistics. This challenge has received less attention so far, and it is the focus of this work. We present and discuss design choices to enable a STA to collects hundreds of per-flow metrics at a multi-10-Gb/s line rate. We leverage a handful of hardware advancements proposed over the last years (e.g., RSS queues, NUMA architecture), and we provide insights on the trade-offs they imply when combined with state-of-the-art packet capture libraries and the multi-process paradigm. We outline the principles to design an optimized STA, and we implement them to engineer DPDKStat, a solution combining the Intel DPDK framework with the traffic analyzer Tstat. Using traces collected from real networks, we demonstrate that DPDKStat achieves 40 Gb/s of aggregated rate with a single COTS PC.

Cloud gaming is a class of services that promises to revolutionize the videogame market. It allows the user to play a videogame with essential equipment while using a remote server for the actual execution. The multimedia content is streamed through the network from the server to the user. Hence, this service requires low latency and a large bandwidth to work properly with low response time and high-definition video. Three of the leading tech companies (Google, Sony, and NVIDIA) entered this market with their products, and others, like Microsoft and Amazon, are also launching their platforms. However, these companies have released little information about their cloud gaming operation and how they utilize the network. In this work, we study cloud gaming services from the network point of view. We collect more than 200 packet traces under different application settings and network conditions from a broadband network to poor mobile network conditions, for 3 cloud gaming services, namely Stadia from Google, GeForce Now from NVIDIA and PS Now from Sony. We analyze the employed protocols and the workload that they impose on the network. We find that GeForce Now and Stadia use the RTP protocol to stream the multimedia content, with the latter relying on the standard WebRTC APIs. Depending on the network and video quality, they result in bandwidth-hungry services consuming up to 45 Mbit/s. PS Now instead uses only undocumented protocols and never exceeds 13 Mbit/s. 4G mobile networks can often sustain these loads, while traditional 3G connections struggle. The systems quickly react to deteriorated network conditions, and packet losses up to 5% do not cause a reduction in resolution.

The Internet and the way people use it are constantly changing. Knowing traffic is crucial for operating the network, understanding users’ needs, and ultimately improving applications. Here, we provide an in-depth longitudinal view of Internet traffic during 5 years (from 2013 to 2017). We take the point of the view of a national-wide ISP and analyze rich flow-level measurements to pinpoint and quantify changes. We observe the traffic, both from a point of view of users and services. We show that an ordinary broadband subscriber downloaded in 2017 more than twice as much as they used to do 5 years before. Bandwidth hungry video services drove this change at the beginning, while recently social messaging applications contribute to increase of data consumption. We study how protocols and service infrastructures evolve over time, highlighting events that may challenge traffic management policies. In the rush to bring servers closer and closer to users, we witness the birth of the sub-millisecond Internet, with caches located directly at ISP edges. The picture we take shows a lively Internet that always evolves and suddenly changes. To support new analyses, we make anonymized data available at https://smartdata.polito.it/five-years-at-the-edge/.

Personalized advertisement has changed the web. It lets websites monetize the content they offer. The downside is the continuous collection of personal information with significant threats to personal privacy. In 2002, the European Union (EU) introduced a first set of regulations on the use of online tracking technologies. It aimed, among other things, to make online tracking mechanisms explicit to increase privacy awareness among users. Amended in 2009, the EU Directive mandates websites to ask for informed consent before using any kind of profiling technology, e.g., cookies. Since 2013, the ePrivacy Directive became mandatory, and each EU Member State transposed it in national legislation. Since then, most of European websites embed a “Cookie Bar”, the most visible effect of the regulation. In this paper, we run a large-scale measurement campaign to check the current implementation status of the EU cookie directive. For this, we use CookieCheck, a simple tool to automatically verify legislation violations. Results depict a shady picture: 49 % of websites do not respect the Directive and install profiling cookies before any user’s consent is given. Beside presenting a detailed picture, this paper casts lights on the difficulty of legislator attempts to regulate the troubled marriage between ad-supported web services and their users. In this picture, online privacy seems to be continuously at stake, and it is hard to reach transparency.

To protect user privacy, legislators have regulated the use of tracking technologies, mandating the acquisition of users’ consent before collecting data. As a result, websites started showing more and more consent management modules–i.e., Consent Banners–the visitors have to interact with to access the website content. Since these banners change the content the browser loads, they challenge web measurement collection, primarily to monitor the extent of tracking technologies, but also to measure web performance. If not correctly handled, Consent Banners prevent crawlers from observing the actual content of the websites. In this paper, we present a comprehensive measurement campaign focusing on popular websites in Europe and the US, visiting both landing and internal pages from different countries around the world. We engineer Priv-Accept , a Web crawler able to accept the Consent Banners, as most users would do in practice. It lets us compare how webpages change before and after accepting such policies, if present. Our results show that all measurements performed ignoring the Consent Banners offer a biased and partial view of the Web. After accepting the privacy policies, web tracking is far more pervasive, and webpages are larger and slower to load.