Systems and Methods for Measuring and Improving End-User Application Performance on Mobile Devices

In today's rapidly growing smartphone society, the time users are spending on their smartphones is continuing to grow and mobile applications are becoming the primary medium for providing services and content to users. With such fast paced growth in smart-phone usage, cellular carriers and internet service providers continuously upgrade their infrastructure to the latest technologies and expand their capacities to improve the performance and reliability of their network and to satisfy exploding user demand for mobile data. On the other side of the spectrum, content providers and e-commerce companies adopt the latest protocols and techniques to provide smooth and feature-rich user experiences on their applications. To ensure a good quality of experience, monitoring how applications perform on users' devices is necessary. Often, network and content providers lack such visibility into the end-user application performance. In this dissertation, we demonstrate that having visibility into the end-user perceived performance, through system design for efficient and coordinated active and passive measurements of end-user application and network performance, is crucial for detecting, diagnosing, and addressing performance problems on mobile devices. My dissertation consists of three projects to support this statement. First, to provide such continuous monitoring on smartphones with constrained resources that operate in such a highly dynamic mobile environment, we devise efficient, adaptive, and coordinated systems, as a platform, for active and passive measurements of end-user performance. Second, using this platform and other passive data collection techniques, we conduct an in-depth user trial of mobile multipath to understand how Multipath TCP (MPTCP) performs in practice. Our measurement study reveals several limitations of MPTCP. Based on the insights gained from our measurement study, we propose two different schemes to address the identified limitations of MPTCP. Last, we show how to provide visibility into the end- user application performance for internet providers and in particular home WiFi routers by passively monitoring users' traffic and utilizing per-app models mapping various network quality of service (QoS) metrics to the application performance.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146014/1/ashnik_1.pd

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

A Cross-layer Approach for MPTCP Path Management in Heterogeneous Vehicular Networks

Multipath communication has recently arisen as a promising tool to address reliable communication in vehicular networks. The architecture of Multipath TCP (MPTCP) is designed to facilitate concurrent utilization of multiple network interfaces, thereby enabling the system to optimize network throughput. In the context of vehicular environments, MPTCP offers a promising solution for seamless roaming, as it enables the system to maintain a stable connection by switching between available network interfaces. This paper investigates the suitability of MPTCP to support resilient and efficient Vehicleto-Infrastructure (V2I) communication over heterogeneous networks. First, we identify and discuss several challenges that arise in heterogeneous vehicular networks, including issues such as Head-of-Line (HoL) blocking and service interruptions during handover events. Then, we propose a cross-layer path management scheme for MPTCP, that leverages real-time network information to improve the reliability and efficiency of multipath vehicular communication. Our emulation results demonstrate that the proposed scheme not only achieves seamless mobility across heterogeneous networks but also significantly reduces handover latency, packet loss, and out-of-order packet delivery. These improvements have a direct impact on the quality of experience for vehicular users, as they lead to lower application layer delay and higher throughput

Improving video streaming experience through network measurements and analysis

Multimedia traffic dominates today’s Internet. In particular, the most prevalent traffic carried over wired and wireless networks is video. Most popular streaming providers (e.g. Netflix, Youtube) utilise HTTP adaptive streaming (HAS) for video content delivery to end-users. The power of HAS lies in the ability to change video quality in real time depending on the current state of the network (i.e. available network resources). The main goal of HAS algorithms is to maximise video quality while minimising re-buffering events and switching between different qualities. However, these requirements are opposite in nature, so striking a perfect blend is challenging, as there is no single widely accepted metric that captures user experience based on the aforementioned requirements. In recent years, researchers have put a lot of effort into designing subjectively validated metrics that can be used to map quality, re-buffering and switching behaviour of HAS players to the overall user experience (i.e. video QoE). This thesis demonstrates how data analysis can contribute in improving video QoE. One of the main characteristics of mobile networks is frequent throughput fluctuations. There are various underlying factors that contribute to this behaviour, including rapid changes in the radio channel conditions, system load and interaction between feedback loops at the different time scales. These fluctuations highlight the challenge to achieve a high video user experience. In this thesis, we tackle this issue by exploring the possibility of throughput prediction in cellular networks. The need for better throughput prediction comes from data-based evidence that standard throughput estimation techniques (e.g. exponential moving average) exhibit low prediction accuracy. Cellular networks deploy opportunistic exponential scheduling algorithms (i.e. proportional-fair) for resource allocation among mobile users/devices. These algorithms take into account a user’s physical layer information together with throughput demand. While the algorithm itself is proprietary to the manufacturer, physical layer and throughput information are exchanged between devices and base stations. Availability of this information allows for a data-driven approach for throughput prediction. This thesis utilises a machine-learning approach to predict available throughput based on measurements in the near past. As a result, a prediction accuracy with an error less than 15% in 90% of samples is achieved. Adding information from other devices served by the same base station (network-based information) further improves accuracy while lessening the need for a large history (i.e. how far to look into the past). Finally, the throughput prediction technique is incorporated to state-of-the-art HAS algorithms. The approach is validated in a commercial cellular network and on a stock mobile device. As a result, better throughput prediction helps in improving user experience up to 33%, while minimising re-buffering events by up to 85%. In contrast to wireless networks, channel characteristics of the wired medium are more stable, resulting in less prominent throughput variations. However, all traffic traverses through network queues (i.e. a router or switch), unlike in cellular networks where each user gets a dedicated queue at the base station. Furthermore, network operators usually deploy a simple first-in-first-out queuing discipline at queues. As a result, traffic can experience excessive delays due to the large queue sizes, usually deployed in order to minimise packet loss and maximise throughput. This effect, also known as bufferbloat, negatively impacts delay-sensitive applications, such as web browsing and voice. While there exist guidelines for modelling queue size, there is no work analysing its impact on video streaming traffic generated by multiple users. To answer this question, the performance of multiple videos clients sharing a bottleneck link is analysed. Moreover, the analysis is extended to a realistic case including heterogeneous round-trip-time (RTT) and traffic (i.e. web browsing). Based on experimental results, a simple two queue discipline is proposed for scheduling heterogeneous traffic by taking into account application characteristics. As a result, compared to the state-of-the-art Active Queue Management (AQM) discipline, Controlled Delay Management (CoDel), the proposed discipline decreases median Page Loading Time (PLT) of web traffic by up to 80% compared to CoDel, with no significant negative impact on video QoE

Web Content Delivery Optimization

Milliseconds matters, when they’re counted. If we consider the life of the universe into one single year, then on 31 December at 11:59:59.5 PM, “speed” was transportation’s concern, and now after 500 milliseconds it is web’s, and no one knows whose concern it would be in coming milliseconds, but at this very moment; this thesis proposes an optimization method, mainly for content delivery on slow connections. The method utilizes a proxy as a middle box to fetch the content; requested by a client, from a single or multiple web servers, and bundles all of the fetched image content types that fits into the bundling policy; inside a JavaScript file in Base64 format. This optimization method reduces the number of HTTP requests between the client and multiple web servers as a result of its proposed bundling solution, and at the same time optimizes the HTTP compression efficiency as a result of its proposed method of aggregative textual content compression. Page loading time results of the test web pages; which were specially designed and developed to capture the optimum benefits of the proposed method; proved up to 81% faster page loading time for all connection types. However, other tests in non-optimal situations such as webpages which use “Lazy Loading” techniques, showed just 35% to 50% benefits, that is only achievable on 2G and 3G connections (0.2 Mbps – 15 Mbps downlink) and not faster connections

Unmanned Aerial Vehicles (UAVs) as on-demand QoS enabler for Multimedia Applications in Smart Cities

The evolution of drones and similar small wingspan UAVs has resulted in their use in many commercial applications. This has allowed investigating the potential use of drones in the context of Internet of Things. In the recent past, there is ample evidence indicating the use of UAVs as a means to supplement mobile infrastructure to extend it for surveillance, monitoring, data collection and providing on-demand network access capabilities. This paper explores the potential of UAVs to act as on-demand QoS enablers for TCP-based applications within Smart Cities, particularly those applications that require low connection delays, reliability and high throughputs such as multimedia streaming.Many multimedia rich applications, such as live streaming, multi-player online gaming are mostly tied down to fixed-line broadband infrastructure. Mobile cloud technologies and Mobile Edge Computing (MEC) address the challenge by bringing the computing, storage and networking resources to the edge and integrating with the base station, thereby providing better content delivery. The paper presents a concept of UAV-based aerial MEC, which hosts a TCP-proxy that acts as an `On-Demand QoS' enabler to TCP-based applications in Smart Cities reducing the overall-connection delays and increasing the throughput thereby enhancing the end-user experience. With the technologies available in literature we demonstrate that a UAV-based aerial MEC with the capability to migrate QoS-enabling processes from the edge to the core and edge to the edge, to support mobile applications, is feasible

Identifying and diagnosing video streaming performance issues

On-line video streaming is an ever evolving ecosystem of services and technologies, where content providers are on a constant race to satisfy the users' demand for richer content and higher bitrate streams, updated set of features and cross-platform compatibility. At the same time, network operators are required to ensure that the requested video streams are delivered through the network with a satisfactory quality in accordance with the existing Service Level Agreements (SLA). However, tracking and maintaining satisfactory video Quality of Experience (QoE) has become a greater challenge for operators than ever before. With the growing popularity of content engagement on handheld devices and over wireless connections, new points-of-failure have added to the list of failures that can affect the video quality. Moreover, the adoption of end-to-end encryption by major streaming services has rendered previously used QoE diagnosis methods obsolete. In this thesis, we identify the current challenges in identifying and diagnosing video streaming issues and we propose novel approaches in order to address them. More specifically, the thesis initially presents methods and tools to identify a wide array of QoE problems and the severity with which they affect the users' experience. The next part of the thesis deals with the investigation of methods to locate under-performing parts of the network that lead to drop of the delivered quality of a service. In this context, we propose a data-driven methodology for detecting the under performing areas of cellular network with sub-optimal Quality of Service (QoS) and video QoE. Moreover, we develop and evaluate a multi-vantage point framework that is capable of diagnosing the underlying faults that cause the disruption of the user's experience. The last part of this work, further explores the detection of network performance anomalies and introduces a novel method for detecting such issues using contextual information. This approach provides higher accuracy when detecting network faults in the presence of high variation and can benefit providers to perform early detection of anomalies before they result in QoE issues.La distribución de vídeo online es un ecosistema de servicios y tecnologías, donde los proveedores de contenidos se encuentran en una carrera continua para satisfacer las demandas crecientes de los usuarios de más riqueza de contenido, velocidad de transmisión, funcionalidad y compatibilidad entre diferentes plataformas. Asimismo, los operadores de red deben asegurar que los contenidos demandados son entregados a través de la red con una calidad satisfactoria según los acuerdos existentes de nivel de servicio (en inglés Service Level Agreement o SLA). Sin embargo, la monitorización y el mantenimiento de un nivel satisfactorio de la calidad de experiencia (en inglés Quality of Experience o QoE) del vídeo online se ha convertido en un reto mayor que nunca para los operadores. Dada la creciente popularidad del consumo de contenido con dispositivos móviles y a través de redes inalámbricas, han aparecido nuevos puntos de fallo que se han añadido a la lista de problemas que pueden afectar a la calidad del vídeo transmitido. Adicionalmente, la adopción de sistemas de encriptación extremo a extremo, por parte de los servicios más importantes de distribución de vídeo online, ha dejado obsoletos los métodos existentes de diagnóstico de la QoE. En esta tesis se identifican los retos actuales en la identificación y diagnóstico de los problemas de transmisión de vídeo online, y se proponen nuevas soluciones para abordar estos problemas. Más concretamente, inicialmente la tesis presenta métodos y herramientas para identificar un conjunto amplio de problemas de QoE y la severidad con los que estos afectan a la experiencia de los usuarios. La siguiente parte de la tesis investiga métodos para localizar partes de la red con un rendimiento bajo que resultan en una disminución de la calidad del servicio ofrecido. En este contexto, se propone una metodología basada en el análisis de datos para detectar áreas de la red móvil que ofrecen un nivel subóptimo de calidad de servicio (en inglés Quality of Service o QoS) y QoE. Además, se desarrolla y se evalúa una solución basada en múltiples puntos de medida que es capaz de diagnosticar los problemas subyacentes que causan la alteración de la experiencia de usuario. La última parte de este trabajo explora adicionalmente la detección de anomalías de rendimiento de la red y presenta un nuevo método para detectar estas situaciones utilizando información contextual. Este enfoque proporciona una mayor precisión en la detección de fallos de la red en presencia de alta variabilidad y puede ayudar a los proveedores a la detección precoz de anomalías antes de que se conviertan en problemas de QoE.La distribució de vídeo online és un ecosistema de serveis i tecnologies, on els proveïdors de continguts es troben en una cursa continua per satisfer les demandes creixents del usuaris de més riquesa de contingut, velocitat de transmissió, funcionalitat i compatibilitat entre diferents plataformes. A la vegada, els operadors de xarxa han d’assegurar que els continguts demandats són entregats a través de la xarxa amb una qualitat satisfactòria segons els acords existents de nivell de servei (en anglès Service Level Agreement o SLA). Tanmateix, el monitoratge i el manteniment d’un nivell satisfactori de la qualitat d’experiència (en anglès Quality of Experience o QoE) del vídeo online ha esdevingut un repte més gran que mai per als operadors. Donada la creixent popularitat del consum de contingut amb dispositius mòbils i a través de xarxes sense fils, han aparegut nous punts de fallada que s’han afegit a la llista de problemes que poden afectar a la qualitat del vídeo transmès. Addicionalment, l’adopció de sistemes d’encriptació extrem a extrem, per part dels serveis més importants de distribució de vídeo online, ha deixat obsolets els mètodes existents de diagnòstic de la QoE. En aquesta tesi s’identifiquen els reptes actuals en la identificació i diagnòstic dels problemes de transmissió de vídeo online, i es proposen noves solucions per abordar aquests problemes. Més concretament, inicialment la tesi presenta mètodes i eines per identificar un conjunt ampli de problemes de QoE i la severitat amb la que aquests afecten a la experiència dels usuaris. La següent part de la tesi investiga mètodes per localitzar parts de la xarxa amb un rendiment baix que resulten en una disminució de la qualitat del servei ofert. En aquest context es proposa una metodologia basada en l’anàlisi de dades per detectar àrees de la xarxa mòbil que ofereixen un nivell subòptim de qualitat de servei (en anglès Quality of Service o QoS) i QoE. A més, es desenvolupa i s’avalua una solució basada en múltiples punts de mesura que és capaç de diagnosticar els problemes subjacents que causen l’alteració de l’experiència d’usuari. L’última part d’aquest treball explora addicionalment la detecció d’anomalies de rendiment de la xarxa i presenta un nou mètode per detectar aquestes situacions utilitzant informació contextual. Aquest enfoc proporciona una major precisió en la detecció de fallades de la xarxa en presencia d’alta variabilitat i pot ajudar als proveïdors a la detecció precoç d’anomalies abans de que es converteixin en problemes de QoE.Postprint (published version