Browsing by Author "Walelgne, Ermias Andargie"
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- Measuring Web Quality of Experience in Cellular Networks
A4 Artikkeli konferenssijulkaisussa(2019-03) Asrese, Alemnew; Walelgne, Ermias Andargie; Bajpai, Vaibhav; Lutu, Andra; Alay, Ozgu; Ott, JörgMeasuring and understanding the end-user browsing Quality of Experience (QoE) is crucial to Mobile Network Operators (MNOs) to retain their customers and increase revenue. MNOs often use traffic traces to detect the bottlenecks and study their end-users experience. Recent studies show that Above The Fold (ATF) time better approximates the user browsing QoE compared to traditional metrics such as Page Load Time (PLT). This work focuses on developing a methodology to measure the web browsing QoE over operational Mobile Broadband (MBB) networks. We implemented a web performance measurement tool WebLAR (it stands for Web Latency And Rendering) that measures web Quality of Service (QoS) such as TCP connect time, and Time To First Byte (TTFB) and web QoE metrics including PLT and ATF time. We deployed WebLAR on 128 MONROE (a European-wide mobile measurement platform) nodes, and conducted two weeks long (May and July 2018) web measurement campaign towards eight websites from six operational MBB networks. The result shows that, in the median case, the TCP connect time and TTFB in Long Term Evolution (LTE) networks are, respectively, 160% and 30% longer than fixed-line networks. The DNS lookup time and TCP connect time of the websites varies significantly across MNOs. Most of the websites do not show a significant difference in PLT and ATF time across operators. However, Yahoo shows longer ATF time in Norwegian operators than that of the Swedish operators. Moreover, user mobility has a small impact on the ATF time of the websites. Furthermore, the website design should be taken into consideration when approximating the ATF time. - On the sensitivity of geo-based content sharing to location errors
A4 Artikkeli konferenssijulkaisussa(2017-03-28) Ott, Jörg; Karkkainen, Ljubica; Walelgne, Ermias Andargie; Keranen, Ari; Hyytia, Esa; Kangasharju, JussiA number of opportunistic content sharing services were developed that exploit device-to-device contacts for infrastructure-less operation. All of them depend, like geo-based ad-hoc routing protocols, on mobile devices knowing their respective positions to accurately perform data replication. In this paper, we explore the impact of different types of location errors on the performance of such a service. We use a GPS error distribution for mobiles derived from real-world measurements, consider different frequencies for GPS readings, and account for only subsets of mobile devices actively using GPS. We carry out extensive simulation studies using synthetic mobility models as well as real-world traces to assess the impact of different types of errors. We find that, overall, opportunistic content sharing is quite robust provided that a sufficient number of nodes support GPS and allow the others to have a rough estimate of where they are. Whether or not the GPS position is prone to errors affects some scenarios and is almost negligible in others. - Performance and Usage Patterns of Mobile Networks
School of Electrical Engineering | Doctoral dissertation (article-based)(2021) Walelgne, Ermias AndargieThe popularity of mobile devices and the availability of various services over mobile cellular networks has increased over the past twenty years. Over time, mobile cellular network technologies have evolved, and the performance of wireless links from mobile devices to the core networks is increasing. Mobile applications and services require differ- ent network qualities to meet users’ expectations and increase the Quality of Experience (QoE). To support the increased number of users, and to deliver the capacity required by applications, mobile networks have become complex systems. The demand for high-quality experience in mobile cellular networks is in the interest of both end-users and providers. However, mobile network performance is affected by a multitude of network features. This includes radio technology, network bandwidth and coverage, signal strength, mobility, throughput, latency, and data usage patterns of users. This thesis analyzes mobile cellular network performance and usage patterns. We apply different data analysis methods and use various datasets collected through crowdsourcing and testbeds. We study various features of mobile networks and their effect on mobile network performance. We propose an estimation method for QoE in web browsing and discuss factors affecting web-flows performance in mobile networks. We present different models based on machine learning that predict network throughput, cluster, and classify mobile users’ data usage patterns. This thesis contributes to the evolving mobile networks by studying various network features that determine the performance of mobile networks and the data usage patterns of mobile users. The large-scale crowdsourced mobile network measurement datasets provide valuable input for understanding factors affecting the performance and quality of mobile networks. The study on the data usage patterns of mobile users provides significant input for understanding mobile users’ data usage patterns and behavior across different countries. The classification model on network stability and data usage patterns can be valuable input for network resource optimization. The study conducted on the feasibility of teleoperated driving and correlation-based network feature mapping shows how crowd-sourced datasets can be used to analyze different uses cases in mobile networks. - Understanding Data Usage Patterns of Geographically Diverse Mobile Users
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2021-09) Walelgne, Ermias Andargie; Asrese, Alemnew Sheferaw; Manner, Jukka; Bajpai, Vaibhav; Ott, JorgThe 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.