We propose a theoretically and practically improved density-based, hierarchical clustering method, providing a clustering hierarchy from which a simplified tree of significant clusters can be constructed. For obtaining a “flat” partition consisting of only the most significant clusters (possibly corresponding to different density thresholds), we propose a novel cluster stability measure, formalize the problem of maximizing the overall stability of selected clusters, and formulate an algorithm that computes an optimal solution to this problem. We demonstrate that our approach outperforms the current, state-of-the-art, density-based clustering methods on a wide variety of real world data.

Density-based clustering based on hierarchical density estimates

Campus traffic and e-Learning during COVID-19 pandemic

The research community has considered in the past the application of Artificial Intelligence (AI) techniques to control and operate networks A notable example is the Knowledge Plane proposed by DClark et al However, such techniques have not been extensively prototyped or deployed in the field yet In this paper, we explore the reasons for the lack of adoption and posit that the rise of two recent paradigms: Software-Defined Networking (SDN) and Network Analytics (NA), will facilitate the adoption of AI techniques in the context of network operation and control We describe a new paradigm that accommodates and exploits SDN, NA and AI, and provide use-cases that illustrate its applicability and benefits We also present simple experimental results that support, for some relevant use-cases, its feasibility We refer to this new paradigm as Knowledge-Defined Networking (KDN)

Knowledge-Defined Networking

The research community has considered in the past the application of Artificial Intelligence (AI) techniques to control and operate networks. A notable example is the Knowledge Plane proposed by D.Clark et al. However, such techniques have not been extensively prototyped or deployed in the field yet. In this paper, we explore the reasons for the lack of adoption and posit that the rise of two recent paradigms: Software-Defined Networking (SDN) and Network Analytics (NA), will facilitate the adoption of AI techniques in the context of network operation and control. We describe a new paradigm that accommodates and exploits SDN, NA and AI, and provide use cases that illustrate its applicability and benefits. We also present simple experimental results that support its feasibility. We refer to this new paradigm as Knowledge-Defined Networking (KDN).

Time-critical and delay-sensitive multimedia applications require more spectrum and transmission resources. With the provision of cognitive radios (CRs), the underutilized spectrum resources can be exploited to gain more bandwidth for the bandwidth hungry applications (multimedia applications). Cognitive radio networks (CRNs) also have the flexibility to adjust their transmission parameters according to the needs of multimedia services or applications. For this reason, wireless multimedia cognitive radio networks (WMCRNs) have gained much attentions in today’s research domain. In this paper, we present a comprehensive survey of WMCRNs. Various multimedia applications supported by CRNs, and various CR-based wireless networks are surveyed. We highlight the routing and link layer protocols used for WMCRNs. We cover the quality-of-experience design and security requirements for transmitting multimedia content over CRNs. We provide an in-depth study of white space, television white space, and cross-layer designs that have been used for WMCRNs. We also survey the major spectrum sensing approaches used for the communications of bandwidth hungry and time-critical data over CRNs.

Wireless Multimedia Cognitive Radio Networks: A Comprehensive Survey

We present BGPStream, an open-source software framework for the analysis of both historical and real-time Border Gateway Protocol (BGP) measurement data. Although BGP is a crucial operational component of the Internet infrastructure, and is the subject of research in the areas of Internet performance, security, topology, protocols, economics, etc., there is no efficient way of processing large amounts of distributed and/or live BGP measurement data. BGPStream fills this gap, enabling efficient investigation of events, rapid prototyping, and building complex tools and large-scale monitoring applications (e.g., detection of connectivity disruptions or BGP hijacking attacks). We discuss the goals and architecture of BGPStream. We apply the components of the framework to different scenarios, and we describe the development and deployment of complex services for global Internet monitoring that we built on top of it.

/pdf/bgpstream-a-software-framework-for-live-and-historical-bgp-1ma9lv4erx.pdf

BGPStream: A Software Framework for Live and Historical BGP Data Analysis

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/ .

Five Years at the Edge: Watching Internet From the ISP Network

Cloud gaming is a new class of services that promises to revolutionize the videogame market. It allows the user to play a videogame with basic equipment while using a remote server for the actual execution. The multimedia content is streamed through the network from the server to the user. 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 own products, and others, like Microsoft and Amazon, are planning to launch their own platforms in the near future. However, these companies released so far little information about their cloud gaming operation and how they utilize the network. In this work, we study these new cloud gaming services from the network point of view. We collect more than 200 packet traces under different application settings and 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 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. They result in bandwidth-hungry and consume up to 45 Mbit/s, depending on the network and video quality. PS Now instead uses only undocumented protocols and never exceeds 13 Mbit/s.

A network analysis on cloud gaming: Stadia, GeForce Now and PSNow

The Internet and the way people use it are constantly changing. Knowing traffic is crucial for operating the network, understanding users' need, and ultimately improving applications. Here, we provide an in-depth longitudinal view of Internet traffic in the last 5 years (from 2013 to 2017). We take the point of the view of a national-wide ISP and analyze flow-level rich measurements to pinpoint and quantify trends. We evaluate the providers' costs in terms of traffic consumption by users and services. We show that an ordinary broadband subscriber nowadays downloads more than twice as much as they used to do 5 years ago. Bandwidth hungry video services drive this change, while social messaging applications boom (and vanish) at incredible pace. We study how protocols and service infrastructures evolve over time, highlighting unpredictable events that may hamper 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.

https://dl.acm.org/doi/pdf/10.1145/3281411.3281433

Five years at the edge: watching internet from the ISP network

Understanding the behavior of a network from a large scale traffic dataset is a challenging problem. Big data frameworks offer scalable algorithms to extract information from raw data, but often require a sophisticated fine-tuning and a detailed knowledge of machine learning algorithms. To streamline this process, we propose self-learning insightful network analyzer (SeLINA), a generic, self-tuning, simple tool to extract knowledge from network traffic measurements. SeLINA includes different data analytics techniques providing self-learning capabilities to state-of-the-art scalable approaches, jointly with parameter auto-selection to off-load the network expert from parameter tuning. We combine both unsupervised and supervised approaches to mine data with a scalable approach. SeLINA embeds mechanisms to check if the new data fits the model, to detect possible changes in the traffic, and to, possibly automatically, trigger model rebuilding. The result is a system that offers human-readable models of the data with minimal user intervention, supporting domain experts in extracting actionable knowledge and highlighting possibly meaningful interpretations. SeLINA’s current implementation runs on Apache Spark. We tested it on large collections of real-world passive network measurements from a nationwide ISP, investigating YouTube, and P2P traffic. The experimental results confirmed the ability of SeLINA to provide insights and detect changes in the data that suggest further analyses.

/pdf/selina-a-self-learning-insightful-network-analyzer-3xop3ybuxa.pdf

Danilo Giordano

Papers

BGPStream: A Software Framework for Live and Historical BGP Data Analysis

Five Years at the Edge: Watching Internet From the ISP Network

A network analysis on cloud gaming: Stadia, GeForce Now and PSNow

Five years at the edge: watching internet from the ISP network

SeLINA: A Self-Learning Insightful Network Analyzer