Proteus:Network-aware Web Browsing on Heterogeneous Mobile Systems

We present Proteus, a novel network-aware approach for optimizing web browsing on heterogeneous multi-core mobile systems. It employs machine learning techniques to predict which of the heterogeneous cores to use to render a given webpage and the operating frequencies of the processors. It achieves this by first learning offline a set of predictive models for a range of typical networking environments. A learnt model is then chosen at runtime to predict the optimal processor configuration, based on the web content, the network status and the optimization goal. We evaluate Proteus by implementing it into the open-source Chromium browser and testing it on two representative ARM big.LITTLE mobile multi-core platforms. We apply Proteus to the top 1,000 popular websites across seven typical network environments. Proteus achieves over 80% of best available performance. It obtains, on average, over 17% (up to 63%), 31% (up to 88%), and 30% (up to 91%) improvement respectively for load time, energy consumption and the energy delay product, when compared to two state-of-the-art approaches

Web Quality of Experience Measurement: Metrics, Methods and Tools

Defence is held on 2.7.2021 14:00 – 18:00 Zoom https://aalto.zoom.us/j/5066362673?pwd=YTc0aDZwUlNhNW5ZMGYrRUExZ3Y4UT09The web is one of the dominant applications on the Internet. Over the last three decades, the web has been evolving in terms of content types, supporting technologies, content provisioning, and access protocols. Similarly, the users' demands for fast and reliable web access have been also growing. Understanding the user browsing Quality of Experience (QoE) is of interest to content and service providers to deliver a quality service. However, the subjective nature of QoE makes it challenging to measure the web user experience on a large scale. Due to this, Quality of Service (QoS) metrics that can be measured on different layers of the web stack have been used to approximate the user experience. In this thesis, we propose a method to calculate an objective web QoE metric that better approximates the user experience. We design and implement a measurement system and tool that can be used on a large scale. We discuss the validation of the measurement system and benchmark the system performance. We present results from measurements that have been conducted to understand the web performance and QoE both from fixed-line and cellular networks. We also discuss modeling the web QoE from the QoS metrics using existing export models (e.g., ITU-T and IQX), and machine learning algorithms (e.g., SVR, CART, BOOST). This thesis contributes to the effort towards understanding, designing, and managing infrastructure to provide improved web QoE. Web users and content and service providers can use the methodology we have proposed and the tools we have designed to understand and troubleshoot possible bottlenecks for poor user experience. For instance, Internet Service Providers (ISPs) can deploy our tools on customer premises in their subscriber base and monitor their end-user web QoE. ISPs can use this for efficient capacity planning, network design, and web traffic management towards popular Content Delivery Networks (CDNs). The work on modeling web QoE shows that the expert models and machine learning-based models have comparable degree of performance accuracy. This thesis also shows that the expert models can accommodate new time-related metrics beyond the web latency metrics

Understanding Data Usage Patterns of Geographically Diverse Mobile Users

The increasing trend of the traffic demand from mobile users and the presence of limited resources creates a challenge for network resource management. Understanding the data usage pattern and traffic demand of mobile users is a way forward to enable data-driven network resource management. However, due to the complex nature of mobile networks, understanding and characterizing data usage pattern of mobile users is a daunting task. In this work, we investigate and characterize data usage patterns and behavior of users in mobile networks. We leverage a dataset (similar to 340 M records) collected through a crowd-based mobile network measurement platform - Netradar - across six countries. We elucidate different network factors and study how they affect the data usage patterns by taking mobile users in Finland as a use case. We perform a comparison on data usage patterns of mobile users across six countries by considering total data consumption, network access, the number of sessions created per user, throughput,and user satisfaction level on services. We show that data usage behavior of users over a mobile network is primarily driven by user mobility, the type of data subscription plan marketed by Mobile Network Operators (MNOs), network congestion, and network coverage. Besides, the data usage patterns over different network technologies (e.g., preferring cellular over WiFi) and the percentage of users accessing congested networks vary by country; mostly due to the market pricing strategy and radio coverage. However, the overall data consumption (cellular and WiFi) is comparatively similar in most of the countries we studied.Peer reviewe