research-article

Causal impact of Android go on mobile web performance

Authors:

Muhammad Abdullah,

Zafar Ayyub Qazi,

Ihsan Ayyub QaziAuthors Info & Claims

IMC '22: Proceedings of the 22nd ACM Internet Measurement Conference

Pages 113 - 129

https://doi.org/10.1145/3517745.3561456

Published: 25 October 2022 Publication History

Abstract

The rapid growth in the number of entry-level smartphones and mobile broadband subscriptions in developing countries has served as a motivation for several projects focused on improving mobile users' quality of experience (QoE). One such initiative is the development of Android Go, a customized operating system designed to run over entry-level smartphones. Today, more than 80% entry-level Android smartphones run Android Go. Despite its growing popularity, its effectiveness in improving the Web QoE remains unclear. This paper presents the first independent empirical analysis of Android Go's causal impact on mobile Web performance. We use a combination of controlled experiments and a set of methodological approaches from the econometrics literature to find unbiased estimates of the average causal effect. Our analysis provides insights that have implications for different stakeholders in the ecosystem of entry-level devices.

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Cited By

Munir MSaeed MAli MQazi ZRaza AQazi I(2023)Learning Fast and Slow: Towards Inclusive Federated LearningMachine Learning and Knowledge Discovery in Databases: Research Track10.1007/978-3-031-43415-0_23(384-401)Online publication date: 18-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-43415-0_23

Index Terms

Causal impact of Android go on mobile web performance

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cover image ACM Conferences

IMC '22: Proceedings of the 22nd ACM Internet Measurement Conference

October 2022

796 pages

ISBN:9781450392594

DOI:10.1145/3517745

General Chairs:
Chadi Barakat
Inria, Université Côte d'Azur
,
Cristel Pelsser
University of Strasbourg
,
Program Chairs:
Theophilus A. Benson
Brown University
,
David Choffnes
Northeastern University

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Published: 25 October 2022

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Cited By

Munir MSaeed MAli MQazi ZRaza AQazi I(2023)Learning Fast and Slow: Towards Inclusive Federated LearningMachine Learning and Knowledge Discovery in Databases: Research Track10.1007/978-3-031-43415-0_23(384-401)Online publication date: 18-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-43415-0_23

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