A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics

A growing number of video streaming networks are incorporating machine learning (ML) applications. The growth of video streaming services places enormous pressure on network and video content providers who need to proactively maintain high levels of video quality. ML has been applied to predict the quality of video streams. Quality of delivery (QoD) measurements, which capture the end-to-end performances of network services, have been leveraged in video quality prediction. The drive for end-to-end encryption, for privacy and digital rights management, has brought about a lack of visibility for operators who desire insights from video quality metrics. In response, numerous solutions have been proposed to tackle the challenge of video quality prediction from QoD-derived metrics. This survey provides a review of studies that focus on ML techniques for predicting the QoD metrics in video streaming services. In the context of video quality measurements, we focus on QoD metrics, which are not tied to a particular type of video streaming service. Unlike previous reviews in the area, this contribution considers papers published between 2016 and 2021. Approaches for predicting QoD for video are grouped under the following headings: (1) video quality prediction under QoD impairments, (2) prediction of video quality from encrypted video streaming traffic, (3) predicting the video quality in HAS applications, (4) predicting the video quality in SDN applications, (5) predicting the video quality in wireless settings, and (6) predicting the video quality in WebRTC applications. Throughout the survey, some research challenges and directions in this area are discussed, including (1) machine learning over deep learning; (2) adaptive deep learning for improved video delivery; (3) computational cost and interpretability; (4) self-healing networks and failure recovery. The survey findings reveal that traditional ML algorithms are the most widely adopted models for solving video quality prediction problems. This family of algorithms has a lot of potential because they are well understood, easy to deploy, and have lower computational requirements than deep learning techniques

Big Data for Traffic Monitoring and Management

The last two decades witnessed tremendous advances in the Information and Communications Technologies. Beside improvements in computational power and storage capacity, communication networks carry nowadays an amount of data which was not envisaged only few years ago. Together with their pervasiveness, network complexity increased at the same pace, leaving operators and researchers with few instruments to understand what happens in the networks, and, on the global scale, on the Internet. Fortunately, recent advances in data science and machine learning come to the rescue of network analysts, and allow analyses with a level of complexity and spatial/temporal scope not possible only 10 years ago. In my thesis, I take the perspective of an Internet Service Provider (ISP), and illustrate challenges and possibilities of analyzing the traffic coming from modern operational networks. I make use of big data and machine learning algorithms, and apply them to datasets coming from passive measurements of ISP and University Campus networks. The marriage between data science and network measurements is complicated by the complexity of machine learning algorithms, and by the intrinsic multi-dimensionality and variability of this kind of data. As such, my work proposes and evaluates novel techniques, inspired from popular machine learning approaches, but carefully tailored to operate with network traffic

Big Data for Traffic Monitoring and Management

The last two decades witnessed tremendous advances in the Information and Com- munications Technologies. Beside improvements in computational power and storage capacity, communication networks carry nowadays an amount of data which was not envisaged only few years ago. Together with their pervasiveness, network complexity increased at the same pace, leaving operators and researchers with few instruments to understand what happens in the networks, and, on the global scale, on the Internet. Fortunately, recent advances in data science and machine learning come to the res- cue of network analysts, and allow analyses with a level of complexity and spatial/tem- poral scope not possible only 10 years ago. In my thesis, I take the perspective of an In- ternet Service Provider (ISP), and illustrate challenges and possibilities of analyzing the traffic coming from modern operational networks. I make use of big data and machine learning algorithms, and apply them to datasets coming from passive measurements of ISP and University Campus networks. The marriage between data science and network measurements is complicated by the complexity of machine learning algorithms, and by the intrinsic multi-dimensionality and variability of this kind of data. As such, my work proposes and evaluates novel techniques, inspired from popular machine learning approaches, but carefully tailored to operate with network traffic. In this thesis, I first provide a thorough characterization of the Internet traffic from 2013 to 2018. I show the most important trends in the composition of traffic and users’ habits across the last 5 years, and describe how the network infrastructure of Internet big players changed in order to support faster and larger traffic. Then, I show the chal- lenges in classifying network traffic, with particular attention to encryption and to the convergence of Internet around few big players. To overcome the limitations of classical approaches, I propose novel algorithms for traffic classification and management lever- aging machine learning techniques, and, in particular, big data approaches. Exploiting temporal correlation among network events, and benefiting from large datasets of op- erational traffic, my algorithms learn common traffic patterns of web services, and use them for (i) traffic classification and (ii) fine-grained traffic management. My proposals are always validated in experimental environments, and, then, deployed in real opera- tional networks, from which I report the most interesting findings I obtain. I also focus on the Quality of Experience (QoE) of web users, as their satisfaction represents the final objective of computer networks. Again, I show that using big data approaches, the network can achieve visibility on the quality of web browsing of users. In general, the algorithms I propose help ISPs have a detailed view of traffic that flows in their network, allowing fine-grained traffic classification and management, and real-time monitoring of users QoE

Mobile Oriented Future Internet (MOFI)

This Special Issue consists of seven papers that discuss how to enhance mobility management and its associated performance in the mobile-oriented future Internet (MOFI) environment. The first two papers deal with the architectural design and experimentation of mobility management schemes, in which new schemes are proposed and real-world testbed experimentations are performed. The subsequent three papers focus on the use of software-defined networks (SDN) for effective service provisioning in the MOFI environment, together with real-world practices and testbed experimentations. The remaining two papers discuss the network engineering issues in newly emerging mobile networks, such as flying ad-hoc networks (FANET) and connected vehicular networks

Online learning on the programmable dataplane

This thesis makes the case for managing computer networks with datadriven methods automated statistical inference and control based on measurement data and runtime observations—and argues for their tight integration with programmable dataplane hardware to make management decisions faster and from more precise data. Optimisation, defence, and measurement of networked infrastructure are each challenging tasks in their own right, which are currently dominated by the use of hand-crafted heuristic methods. These become harder to reason about and deploy as networks scale in rates and number of forwarding elements, but their design requires expert knowledge and care around unexpected protocol interactions. This makes tailored, per-deployment or -workload solutions infeasible to develop. Recent advances in machine learning offer capable function approximation and closed-loop control which suit many of these tasks. New, programmable dataplane hardware enables more agility in the network— runtime reprogrammability, precise traffic measurement, and low latency on-path processing. The synthesis of these two developments allows complex decisions to be made on previously unusable state, and made quicker by offloading inference to the network. To justify this argument, I advance the state of the art in data-driven defence of networks, novel dataplane-friendly online reinforcement learning algorithms, and in-network data reduction to allow classification of switchscale data. Each requires co-design aware of the network, and of the failure modes of systems and carried traffic. To make online learning possible in the dataplane, I use fixed-point arithmetic and modify classical (non-neural) approaches to take advantage of the SmartNIC compute model and make use of rich device local state. I show that data-driven solutions still require great care to correctly design, but with the right domain expertise they can improve on pathological cases in DDoS defence, such as protecting legitimate UDP traffic. In-network aggregation to histograms is shown to enable accurate classification from fine temporal effects, and allows hosts to scale such classification to far larger flow counts and traffic volume. Moving reinforcement learning to the dataplane is shown to offer substantial benefits to stateaction latency and online learning throughput versus host machines; allowing policies to react faster to fine-grained network events. The dataplane environment is key in making reactive online learning feasible—to port further algorithms and learnt functions, I collate and analyse the strengths of current and future hardware designs, as well as individual algorithms

Enabling energy-awareness for internet video

Continuous improvements to the state of the art have made it easier to create, send and receive vast quantities of video over the Internet. Catalysed by these developments, video is now the largest, and fastest growing type of traffic on modern IP networks. In 2015, video was responsible for 70% of all traffic on the Internet, with an compound annual growth rate of 27%. On the other hand, concerns about the growing energy consumption of ICT in general, continue to rise. It is not surprising that there is a significant energy cost associated with these extensive video usage patterns. In this thesis, I examine the energy consumption of typical video configurations during decoding (playback) and encoding through empirical measurements on an experimental test-bed. I then make extrapolations to a global scale to show the opportunity for significant energy savings, achievable by simple modifications to these video configurations. Based on insights gained from these measurements, I propose a novel, energy-aware Quality of Experience (QoE) metric for digital video - the Energy - Video Quality Index (EnVI). Then, I present and evaluate vEQ-benchmark, a benchmarking and measurement tool for the purpose of generating EnVI scores. The tool enables fine-grained resource-usage analyses on video playback systems, and facilitates the creation of statistical models of power usage for these systems. I propose GreenDASH, an energy-aware extension of the existing Dynamic Adaptive Streaming over HTTP standard (DASH). GreenDASH incorporates relevant energy-usage and video quality information into the existing standard. It could enable dynamic, energy-aware adaptation for video in response to energy-usage and user ‘green’ preferences. I also evaluate the subjective perception of such energy-aware, adaptive video streaming by means of a user study featuring 36 participants. I examine how video may be adapted to save energy without a significant impact on the Quality of Experience of these users. In summary, this thesis highlights the significant opportunities for energy savings if Internet users gain an awareness about their energy usage, and presents a technical discussion how this can be achieved by straightforward extensions to the current state of the art

Ohjelmoitava saumaton moniliitettävyys

Our devices have become accustomed to being always connected to the Internet. Our devices from handheld devices, such as smartphones and tablets, to our laptops and even desktop PCs are capable of using both wired and wireless networks, ranging from mobile networks such as 5G or 6G in the future to Wi-Fi, Bluetooth, and Ethernet. The applications running on the devices can use different transport protocols from traditional TCP and UDP to state-of-the-art protocols such as QUIC. However, most of our applications still use TCP, UDP, and other protocols in a similar way as they were originally designed in the 1980s, four decades ago. The transport connections are a single path from the source to the destination, using the end-to-end principle without taking advantage of the multiple available transports. Over the years, there have been a lot of studies on both multihoming and multipath protocols, i.e., allowing transports to use multiple paths and interfaces to the destination. Using these would allow better mobility and more efficient use of available transports. However, Internet ossification has hindered their deployment. One of the main reasons for the ossification is the IPv4 Network Address Translation (NAT) introduced in 1993, which allowed whole networks to be hosted behind a single public IP address. Unfortunately, how this many-to-one translation should be done was not standardized thoroughly, allowing vendors to implement their own versions of NAT. While breaking the end-to-end principle, the different versions of NATs also behave unpredictably when encountering other transport protocols than the traditional TCP and UDP, from forwarding packets without translating the packet headers to even discarding the packets that they do not recognize. Similarly, in the context of multiconnectivity, NATs and other middleboxes such as firewalls and load balancers likely prevent connection establishment for multipath protocols unless they are specially designed to support that particular protocol. One promising avenue for solving these issues is Software-Defined Networking (SDN). SDN allows the forwarding elements of the network to remain relatively simple by separating the data plane from the control plane. In SDN, the control plane is realized through SDN controllers, which control how traffic is forwarded by the data plane. This allows controllers to have full control over the traffic inside the network, thus granting fine-grained control of the connections and allowing faster deployment of new protocols. Unfortunately, SDN-capable network elements are still rare in Small Office / Home Office (SOHO) networks, as legacy forwarding elements that do not support SDN can support the majority of contemporary protocols. The most glaring example is the Wi-Fi networks, where the Access Points (AP) typically do not support SDN, and allow traffic to flow between clients without the control of the SDN controllers. In this thesis, we provide a background on why multiconnectivity is still hard, even though there have been decades worth of research on solving it. We also demonstrate how the same devices that made multiconnectivity hard can be used to bring SDN-based traffic control to wireless and SOHO networks. We also explore how this SDN-based traffic control can be leveraged for building a network orchestrator for controlling and managing networks consisting of heterogeneous devices and their controllers. With the insights provided by the legacy devices and programmable networks, we demonstrate two different methods for providing multiconnectivity; one using network-driven programmability, and one using a userspace library, that brings different multihoming and multipathing methods under one roof.Nykyisin kaikki käyttämämme laitteet ovat käytännössä aina yhteydessä Internettiin. Laitteemme voivat käyttää useita erilaisia yhteystapoja, mukaanlukien sekä langallisia, että langattomia verkkoja, kuten Wi-Fi ja mobiiliverkkoja. Kuitenkin laitteemme käyttävät pääsääntöisesti edelleen tietoliikenneprotokollia, jotka suunniteltiin alunperin 1980-luvulla. Tällöin laitteet pystyivät viestimään suoraan toistensa kanssa ilman, että välissä oli verkkolaitteita, jotka piilottivat osia verkosta taakseen. Tämä näkyy protokollien suunnittelussa siten, että jokaisella yhteydellä on määritetyt lähde- ja kohdeosoitteet. Nykyisin laitteemme käyttävät edelleen samaa yhteysparadigmaa, vaikka ne voisivat niputtaa yhteen useampia tietoliikenneyhteyksiä. Tällöin saisimme paremmin käyttöön verkon tarjoaman suorituskyvyn ja muut ominaisuudet. Vuosien saatossa on kehitetty erilaisia monitie (eng. multipath) ja moniyhteys (eng. multihoming) tietoliikenneprotokollia, joiden avulla laitteet pystyvät käyttämään useampia polkuja verkon yli kohteeseensa. Nämä protokollat eivät kuitenkaan ole vielä yleistyneet, sillä kaikki verkkolaitteet eivät tue niitä. Emme myöskään pysty vaikuttamaan kuin ainoastaan epäsuorasti siihen, mitä yhteyttä laitteemme käyttävät. Yksi ratkaisu on tähän ottaa käyttöön ohjelmallisesti määritetyt verkot (eng. Software-Defined Networking, SDN). SDN on paradigma, jonka avulla verkkoihin voidaan tuoda älykkyyttä ja mahdollistaa mm. tehokkaampi liikenteen reititys verkoissa. Tämän väitöskirjatutkimuksen tarkoituksena on käsitellä moniliitettävyyden ongelmia ja ratkaisuja. Tutkimus valottaa miksi moniliitettävyys on edelleen hankala toteuttaa, sekä esittelee kaksi tekniikkaa toteuttaa moniliitettävyys. Ensimmäinen tekniikka soveltaa ohjelmallisesti määritettyjä verkkoja käyttäen hyväkseen väitöskirjan aikana tehtyä tutkimusta, ja toinen tekniikka kerää saman katon alle useita erilaisia monitie- ja moniyhteysprotokollia yhdeksi moniliitettävyyskirjastoksi. Väitöskirjassa esitellään myös kaksi menetelmää tuoda ohjelmallisesti määritetyt verkot laitteisiin, joita ei ole suunniteltu niitä silmällä pitäen. Näiden menetelmien avulla voidaan hallita ja tuoda uusia ominaisuuksia jo olemassa oleviin verkkoihin. Väitöskirjassa esitellään myös koneoppimista soveltava älykäs järjestelmä, joka havaitsee ja poistaa automaattisesti haavoittuvia laitteita verkosta

XX Workshop de Investigadores en Ciencias de la Computación - WICC 2018 : Libro de actas

Actas del XX Workshop de Investigadores en Ciencias de la Computación (WICC 2018), realizado en Facultad de Ciencias Exactas y Naturales y Agrimensura de la Universidad Nacional del Nordeste, los dìas 26 y 27 de abril de 2018.Red de Universidades con Carreras en Informática (RedUNCI

XX Workshop de Investigadores en Ciencias de la Computación - WICC 2018 : Libro de actas

Actas del XX Workshop de Investigadores en Ciencias de la Computación (WICC 2018), realizado en Facultad de Ciencias Exactas y Naturales y Agrimensura de la Universidad Nacional del Nordeste, los dìas 26 y 27 de abril de 2018.Red de Universidades con Carreras en Informática (RedUNCI

XXIII Congreso Argentino de Ciencias de la Computación - CACIC 2017 : Libro de actas

Trabajos presentados en el XXIII Congreso Argentino de Ciencias de la Computación (CACIC), celebrado en la ciudad de La Plata los días 9 al 13 de octubre de 2017, organizado por la Red de Universidades con Carreras en Informática (RedUNCI) y la Facultad de Informática de la Universidad Nacional de La Plata (UNLP).Red de Universidades con Carreras en Informática (RedUNCI