289,430 research outputs found

    Mobile web and app QoE monitoring for ISPs - from encrypted traffic to speed index through machine learning

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    International audienceWeb browsing is one of the key applications of the Internet. In this paper, we address the problem of mobile Web and App QoE monitoring from the Internet Service Provider (ISP) perspective, relying on in-network, passive measurements. Our study targets the analysis of Web and App QoE in mobile devices, including mobile browsing in smartphones and tablets, as well as mobile apps. As a proxy to Web QoE, we focus on the analysis of the well-known Speed Index (SI) metric. Given the wide adoption of end-to-end encryption, we resort to machine-learning models to infer the SI of individual web page and app loading sessions, using as input only packet level data. Empirical evaluations on a large, multi mobile-device corpus of Web and App QoE measurements for top popular websites and selected apps demonstrate that the proposed solution can properly infer the SI from in-network, encrypted-traffic measurements, relying on learning-based models. Our study also reveals relevant network and web page content characteristics impacting Web QoE in mobile devices, providing a complete overview on the mobile Web and App QoE assessment problem

    Mobile Web: Technologies and issues

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    In the last years we have seen many attempts to transfer the desktop browser experience to the mobile sphere, with poor results. Nonetheless, both devices’ performance improvement and the development of new browsing support technologies are enabling the rise of a new form of mobile Web (also known as Mobile Web 2.0). We detail the characteristics of this mobile Web 2.0, the existing barriers that may impede its growth, as well as the distinctive characteristics of mobile browsing and the various technologies that favour the progress of the mobile Web 2.0, with a special focus in ajax, mashups, widgets and content syndication

    Enabling context-aware HTTP with mobile edge hint

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    Due to dynamic wireless network conditions and heterogeneous mobile web content complexities, web-based content services in mobile network environments always suffer from long loading time. The new HTTP/2.0 protocol only adopts one single TCP connection, but recent research reveals that in real mobile environments, web downloading using single connection will experience long idle time and low bandwidth utilization, in particular with dynamic network conditions and web page characteristics. In this paper, by leveraging the Mobile Edge Computing (MEC) technique, we present the framework of Mobile Edge Hint (MEH), in order to enhance mobile web downloading performances. Specifically, the mobile edge collects and caches the meta-data of frequently visited web pages and also keeps monitoring the network conditions. Upon receiving requests on these popular webpages, the MEC server is able to hint back to the HTTP/2.0 clients on the optimized number of TCP connections that should be established for downloading the content. From the test results on real LTE testbed equipped with MEH, we observed up to 34.5% time reduction and in the median case the improvement is 20.5% compared to the plain over-the-top (OTT) HTTP/2.0 protocol

    Chunking and Extracting Text Content for Mobile Learning: A Query-focused Summarizer Based on Relevance Language Model

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    ICALT is the top-tier international conference in educational technology with excellent academic background and very high level of academic performance. During the conference, I presented a short paper (which is the result of my current research in text summarization via mobile learning) in the conference. I also discussed my research with outstanding scholars from other research groups. I had received many positive feedbacks and useful suggestions from the conference participants. I believe these suggestions will provide me significant further scholarly directions. By attending such high quality conference, I can obtain advanced knowledge in academic research. This knowledge will directly benefit my work at Athabasca University. In short, this A&PDF activity is very helpful to my research and professional development in Athabasca University.Millions of text contents and multimedia published on the Web have potential to be shared as the learning contents. However, mobile learners often feel it difficult to extract useful contents for learning. Manually creating content not only requires a huge effort on the part of the teachers but also creates barriers towards reuse of the content that has already been created for e-Learning. In this paper, a text-based content summarizer is introduced to address an approach to help mobile learners to retrieve and process information more quickly by aligning text-based content size to various mobile characteristics. In this work, probabilistic language modeling techniques are integrated into an extractive text summarization system to fulfill the automatic summary generation for mobile learning. Experimental results have shown that our solution is a proper and efficient approach to help mobile learners to summarize important content quickly
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