Papers by Alemnew Sheferaw Asrese
This work presents a new methodology to measure the performance of web browsing over operational ... more This work presents a new methodology to measure the performance of web browsing over operational Mobile Broadband (MBB) networks. We designed a web performance measurement tool that collects both the web QoS metrics and the web rendering time in a browser window. We used MONROE [1], European wide measurement platform, to deploy our tool and to conduct a web browsing measurement over operational MBB networks. The results from the initial deployment show that different operators across countries and within the same country have a significant difference in web browsing performance (e.g. in the median case TIM is 75 ms faster than I WIND regarding time to first byte (TTFB)).
Lecture Notes in Computer Science, 2019
I am immensely thankful to my families and relatives for their unconditional love and support thr... more I am immensely thankful to my families and relatives for their unconditional love and support through my school life. Last but not least, I would like to thank my better half, Ichalem, for her love and everything else. This thesis is dedicated to my mother Tena Yihunie-who is my role model of perseverance and confidence-and Adisualem-youngest brother, who was born three weeks after this research was started.
Computer Networks, Mar 1, 2021
This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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=YTc0aDZwUlN... more 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
In this paper, we present WePR, a web performance measurement tool for evaluating the download an... more In this paper, we present WePR, a web performance measurement tool for evaluating the download and the rendering time of a website at large scale. The first component of the tool, WebPerf, measures the technical metrics of a website such as DNS lookup time, TCP connection establishment time, page download time from different vantage points. The second component renders the website based on the metrics recorded by the WebPerf and measures the rendering time of the website in a browser window. The user experience on a website is then inferred from the measured rendering time. Along with describing WePR's architectural design and the metrics it measures, we present initial measurement results recorded March to August 2015. The results show that transmission latency, DNS lookup time, the number of HTTP elements affect the rendering time of the website and therefore the end user experience.
IEEE Transactions on Network and Service Management, Sep 1, 2021
The increasing trend of the traffic demand from mobile users and the presence of limited resource... more 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 (∼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.
Computer Networks
Mobile users demand more and more data traffic, yet network resources are limited. This creates a... more Mobile users demand more and more data traffic, yet network resources are limited. This creates a challenge for network resource management. One way of addressing this challenge is by understanding the data usage patterns of mobile users so that resources can be optimally allocated based on user traffic demand and data usage behavior. However, understanding and characterizing the data usage patterns of mobile users is a complex task. In this work, we investigate and characterize users' data usage patterns and behavior in mobile networks. We leverage a dataset (∼113 million records) collected through a crowd-based mobile network measurement platform-Netradar-across five countries. Data usage behavior of users over a cellular network is primarily driven by user mobility, the type of subscription plan marketed by Mobile Network Operators (MNOs), network congestion, and network coverage. We apply an unsupervised machine learning approach to cluster mobile user types by considering different factors such as data consumption, network access type, the number of sessions created per user, throughput, and mobility. By defining data usage pattern of mobile users, we develop a user clustering model and identify three different mobile user groups (clusters). Our clustering model shows that the data usage patterns are unevenly distributed across the five countries studied, characterized by a small number of heavy users consuming the highest volume of data. We show how the types of applications installed by users correlate with data consumption patterns in some countries. Heavy users tend to install more traffic-demanding apps than users from the other two groups-regular and light users. Finally, we trained a classification model using the labeled dataset produced by our aforementioned user clustering method. The model helps classifying mobile users according to their usage patterns (i.e., heavy, regular, and light) with an accuracy of ∼80% in the test dataset.
IEEE INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2018
This work presents a new methodology to measure the performance of web browsing over operational ... more This work presents a new methodology to measure the performance of web browsing over operational Mobile Broadband (MBB) networks. We designed a web performance measurement tool that collects both the web QoS metrics and the web rendering time in a browser window. We used MONROE [1], European wide measurement platform, to deploy our tool and to conduct a web browsing measurement over operational MBB networks. The results from the initial deployment show that different operators across countries and within the same country have a significant difference in web browsing performance (e.g. in the median case TIM is 75 ms faster than I WIND regarding time to first byte (TTFB)).
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2020
This paper presents the results of a survey aimed at understanding the status of Internet measure... more This paper presents the results of a survey aimed at understanding the status of Internet measurement platforms usage, deployment and capabilities in Africa. It presents findings related to prevalence of measurement in the region, the reasons why the different business categories investigated conduct Internet measurement as well as the metrics of interest to these entities. The survey also looked at the popular measurement platforms that the respondents use in their measurement activities as well as the platforms that are hosted by businesses and users in the African region. The survey also recorded responses related to data handling and privacy considerations. A total of 123 responses were received from 34 countries. The survey revealed that Internet measurements are not widely conducted in the region largely due to the inadequacy of deployed measurement platforms, the lack of awareness in the subject, and the lack of relevant skills to carry out the measurement tasks. We outlined some recommendations to remedy these issues.
Passive and Active Measurement, 2018
Page load time (PLT) is still the most common application Quality of Service (QoS) metric to esti... more Page load time (PLT) is still the most common application Quality of Service (QoS) metric to estimate the Quality of Experience (QoE) of Web users. Yet, recent literature abounds with proposals for alternative metrics (e.g., Above The Fold, SpeedIndex and variants) that aim at better estimating user QoE. The main purpose of this work is thus to thoroughly investigate a mapping between established and recently proposed objective metrics and user QoE. We obtain ground truth QoE via user experiments where we collect and analyze 3,400 Web accesses annotated with QoS metrics and explicit user ratings in a scale of 1 to 5, which we make available to the community. In particular, we contrast domain expert models (such as ITU-T and IQX) fed with a single QoS metric, to models trained using our ground-truth dataset over multiple QoS metrics as features. Results of our experiments show that, albeit very simple, expert models have a comparable accuracy to machine learning approaches. Furthermore, the model accuracy improves considerably when building per-page QoE models, which may raise scalability concerns as we discuss.
IEEE Transactions on Network and Service Management, 2019
This paper presents Webget, a measurement tool that measures web Quality of Service (QoS) metrics... more This paper presents Webget, a measurement tool that measures web Quality of Service (QoS) metrics including the DNS lookup time, time to first byte (TTFB) and the download time. Webget also captures web complexity metrics such as the number and the size of objects that make up the website. We deploy the Webget test to measure the web performance of Google, YouTube, and Facebook from 182 SamKnows probes. Using a 3.5year-long (Jan 2014-Jul 2017) dataset, we show that the DNS lookup time of these popular Content Delivery Networks (CDNs) and the download time of Google have improved over time. We also show that the TTFB towards Facebook exhibits worse performance than the Google CDN. Moreover, we show that the number and the size of objects are not the only factors that affect the web download time. We observe that these webpages perform differently across regions and service providers. We also developed a web measurement system, WePR (Web Performance and Rendering) that measures the same web QoS and complexity metrics as Webget, but it also captures the web Quality of Experience (QoE) metrics such as rendering time. WePR has a distributed architecture where the component that measures the web QoS and complexity metrics is deployed on the SamKnows probe, while the rendering time is calculated on a central server. We measured the rendering performance of four websites. We show that in 80% of the cases, the rendering time of the websites is faster than the downloading time. The source code of the WePR system and the dataset is made publicly available.
2016 IEEE Globecom Workshops (GC Wkshps), 2016
In this paper, we present WePR, a web performance measurement tool for evaluating the download an... more In this paper, we present WePR, a web performance measurement tool for evaluating the download and the rendering time of a website at large scale. The first component of the tool, WebPerf, measures the technical metrics of a website such as DNS lookup time, TCP connection establishment time, page download time from different vantage points. The second component renders the website based on the metrics recorded by the WebPerf and measures the rendering time of the website in a browser window. The user experience on a website is then inferred from the measured rendering time. Along with describing WePR's architectural design and the metrics it measures, we present initial measurement results recorded March to August 2015. The results show that transmission latency, DNS lookup time, the number of HTTP elements affect the rendering time of the website and therefore the end user experience.
2015 Seventh International Workshop on Quality of Multimedia Experience (QoMEX), 2015
While the modeling of QoE has made significant advances over the last couple of years, currently ... more While the modeling of QoE has made significant advances over the last couple of years, currently existing models still lack an integration of user behavior aspects and user context factors along with the consideration of appropriate temporal scales. Therefore, the goal of this paper is to present a comprehensive QoE and user behavior model providing a framework which allows joining a multitude of existing modeling approaches under the perspectives of service provider benefit, user well-being and technical system performance. In addition, we discuss the role of a broad range of corresponding influence factors, with a specific emphasis on user and context issues, and illustrate our proposal through a series of related use cases.
IEEE Transactions on Network and Service Management, 2020
The increasing trend of the traffic demand from mobile users and the presence of limited resource... more 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 (∼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.
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Papers by Alemnew Sheferaw Asrese