Optimizing the delivery of multimedia over mobile networks

Mención Internacional en el título de doctorThe consumption of multimedia content is moving from a residential environment to mobile phones. Mobile data traffic, driven mostly by video demand, is increasing rapidly and wireless spectrum is becoming a more and more scarce resource. This makes it highly important to operate mobile networks efficiently. To tackle this, recent developments in anticipatory networking schemes make it possible to to predict the future capacity of mobile devices and optimize the allocation of the limited wireless resources. Further, optimizing Quality of Experience—smooth, quick, and high quality playback—is more difficult in the mobile setting, due to the highly dynamic nature of wireless links. A key requirement for achieving, both anticipatory networking schemes and QoE optimization, is estimating the available bandwidth of mobile devices. Ideally, this should be done quickly and with low overhead. In summary, we propose a series of improvements to the delivery of multimedia over mobile networks. We do so, be identifying inefficiencies in the interconnection of mobile operators with the servers hosting content, propose an algorithm to opportunistically create frequent capacity estimations suitable for use in resource optimization solutions and finally propose another algorithm able to estimate the bandwidth class of a device based on minimal traffic in order to identify the ideal streaming quality its connection may support before commencing playback. The main body of this thesis proposes two lightweight algorithms designed to provide bandwidth estimations under the high constraints of the mobile environment, such as and most notably the usually very limited traffic quota. To do so, we begin with providing a thorough overview of the communication path between a content server and a mobile device. We continue with analysing how accurate smartphone measurements can be and also go in depth identifying the various artifacts adding noise to the fidelity of on device measurements. Then, we first propose a novel lightweight measurement technique that can be used as a basis for advanced resource optimization algorithms to be run on mobile phones. Our main idea leverages an original packet dispersion based technique to estimate per user capacity. This allows passive measurements by just sampling the existing mobile traffic. Our technique is able to efficiently filter outliers introduced by mobile network schedulers and phone hardware. In order to asses and verify our measurement technique, we apply it to a diverse dataset generated by both extensive simulations and a week-long measurement campaign spanning two cities in two countries, different radio technologies, and covering all times of the day. The results demonstrate that our technique is effective even if it is provided only with a small fraction of the exchanged packets of a flow. The only requirement for the input data is that it should consist of a few consecutive packets that are gathered periodically. This makes the measurement algorithm a good candidate for inclusion in OS libraries to allow for advanced resource optimization and application-level traffic scheduling, based on current and predicted future user capacity. We proceed with another algorithm that takes advantage of the traffic generated by short-lived TCP connections, which form the majority of the mobile connections, to passively estimate the currently available bandwidth class. Our algorithm is able to extract useful information even if the TCP connection never exits the slow start phase. To the best of our knowledge, no other solution can operate with such constrained input. Our estimation method is able to achieve good precision despite artifacts introduced by the slow start behavior of TCP, mobile scheduler and phone hardware. We evaluate our solution against traces collected in 4 European countries. Furthermore, the small footprint of our algorithm allows its deployment on resource limited devices. Finally, in an attempt to face the rapid traffic increase, mobile application developers outsource their cloud infrastructure deployment and content delivery to cloud computing services and content delivery networks. Studying how these services, which we collectively denote Cloud Service Providers (CSPs), perform over Mobile Network Operators (MNOs) is crucial to understanding some of the performance limitations of today’s mobile apps. To that end, we perform the first empirical study of the complex dynamics between applications, MNOs and CSPs. First, we use real mobile app traffic traces that we gathered through a global crowdsourcing campaign to identify the most prevalent CSPs supporting today’s mobile Internet. Then, we investigate how well these services interconnect with major European MNOs at a topological level, and measure their performance over European MNO networks through a month-long measurement campaign on the MONROE mobile broadband testbed. We discover that the top 6 most prevalent CSPs are used by 85% of apps, and observe significant differences in their performance across different MNOs due to the nature of their services, peering relationships with MNOs, and deployment strategies. We also find that CSP performance in MNOs is affected by inflated path length, roaming, and presence of middleboxes, but not influenced by the choice of DNS resolver. We also observe that the choice of operator’s Point of Presence (PoP) may inflate by at least 20% the delay towards popular websites.This work has been supported by IMDEA Networks Institute.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Ahmed Elmokashfi.- Secretario: Rubén Cuevas Rumín.- Vocal: Paolo Din

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

Unified architecture of mobile ad hoc network security (MANS) system

In this dissertation, a unified architecture of Mobile Ad-hoc Network Security (MANS) system is proposed, under which IDS agent, authentication, recovery policy and other policies can be defined formally and explicitly, and are enforced by a uniform architecture. A new authentication model for high-value transactions in cluster-based MANET is also designed in MANS system. This model is motivated by previous works but try to use their beauties and avoid their shortcomings, by using threshold sharing of the certificate signing key within each cluster to distribute the certificate services, and using certificate chain and certificate repository to achieve better scalability, less overhead and better security performance. An Intrusion Detection System is installed in every node, which is responsible for colleting local data from its host node and neighbor nodes within its communication range, pro-processing raw data and periodically broadcasting to its neighborhood, classifying normal or abnormal based on pro-processed data from its host node and neighbor nodes. Security recovery policy in ad hoc networks is the procedure of making a global decision according to messages received from distributed IDS and restore to operational health the whole system if any user or host that conducts the inappropriate, incorrect, or anomalous activities that threaten the connectivity or reliability of the networks and the authenticity of the data traffic in the networks. Finally, quantitative risk assessment model is proposed to numerically evaluate MANS security

Reducing Internet Latency : A Survey of Techniques and their Merit

Bob Briscoe, Anna Brunstrom, Andreas Petlund, David Hayes, David Ros, Ing-Jyh Tsang, Stein Gjessing, Gorry Fairhurst, Carsten Griwodz, Michael WelzlPeer reviewedPreprin

Treatment-Based Classi?cation in Residential Wireless Access Points

IEEE 802.11 wireless access points (APs) act as the central communication hub inside homes, connecting all networked devices to the Internet. Home users run a variety of network applications with diverse Quality-of-Service requirements (QoS) through their APs. However, wireless APs are often the bottleneck in residential networks as broadband connection speeds keep increasing. Because of the lack of QoS support and complicated configuration procedures in most off-the-shelf APs, users can experience QoS degradation with their wireless networks, especially when multiple applications are running concurrently. This dissertation presents CATNAP, Classification And Treatment iN an AP , to provide better QoS support for various applications over residential wireless networks, especially timely delivery for real-time applications and high throughput for download-based applications. CATNAP consists of three major components: supporting functions, classifiers, and treatment modules. The supporting functions collect necessary flow level statistics and feed it into the CATNAP classifiers. Then, the CATNAP classifiers categorize flows along three-dimensions: response-based/non-response-based, interactive/non-interactive, and greedy/non-greedy. Each CATNAP traffic category can be directly mapped to one of the following treatments: push/delay, limited advertised window size/drop, and reserve bandwidth. Based on the classification results, the CATNAP treatment module automatically applies the treatment policy to provide better QoS support. CATNAP is implemented with the NS network simulator, and evaluated against DropTail and Strict Priority Queue (SPQ) under various network and traffic conditions. In most simulation cases, CATNAP provides better QoS supports than DropTail: it lowers queuing delay for multimedia applications such as VoIP, games and video, fairly treats FTP flows with various round trip times, and is even functional when misbehaving UDP traffic is present. Unlike current QoS methods, CATNAP is a plug-and-play solution, automatically classifying and treating flows without any user configuration, or any modification to end hosts or applications