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Proteus: Network-aware Web Browsing on Heterogeneous Mobile Systems

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

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Proteus: Network-aware Web Browsing on Heterogeneous Mobile Systems. / Ren, Jie; Wang, Xiaoming; Fang, Jianbin et al.
CoNEXT '18 Proceedings of the 14th International Conference on emerging Networking EXperiments and Technologies. New York: ACM, 2018. p. 379-392.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Ren, J, Wang, X, Fang, J, Feng, Y, Zhu, D, Luo, Z & Wang, Z 2018, Proteus: Network-aware Web Browsing on Heterogeneous Mobile Systems. in CoNEXT '18 Proceedings of the 14th International Conference on emerging Networking EXperiments and Technologies. ACM, New York, pp. 379-392. https://doi.org/10.1145/3281411.3281422

APA

Ren, J., Wang, X., Fang, J., Feng, Y., Zhu, D., Luo, Z., & Wang, Z. (2018). Proteus: Network-aware Web Browsing on Heterogeneous Mobile Systems. In CoNEXT '18 Proceedings of the 14th International Conference on emerging Networking EXperiments and Technologies (pp. 379-392). ACM. https://doi.org/10.1145/3281411.3281422

Vancouver

Ren J, Wang X, Fang J, Feng Y, Zhu D, Luo Z et al. Proteus: Network-aware Web Browsing on Heterogeneous Mobile Systems. In CoNEXT '18 Proceedings of the 14th International Conference on emerging Networking EXperiments and Technologies. New York: ACM. 2018. p. 379-392 doi: 10.1145/3281411.3281422

Author

Ren, Jie ; Wang, Xiaoming ; Fang, Jianbin et al. / Proteus : Network-aware Web Browsing on Heterogeneous Mobile Systems. CoNEXT '18 Proceedings of the 14th International Conference on emerging Networking EXperiments and Technologies. New York : ACM, 2018. pp. 379-392

Bibtex

@inproceedings{9977451106c84bf3918bbc9d9a088aaa,
title = "Proteus: Network-aware Web Browsing on Heterogeneous Mobile Systems",
abstract = "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.",
author = "Jie Ren and Xiaoming Wang and Jianbin Fang and Yansong Feng and Dongxiao Zhu and Zhunchen Luo and Zheng Wang",
year = "2018",
month = dec,
day = "4",
doi = "10.1145/3281411.3281422",
language = "English",
pages = "379--392",
booktitle = "CoNEXT '18 Proceedings of the 14th International Conference on emerging Networking EXperiments and Technologies",
publisher = "ACM",

}

RIS

TY - GEN

T1 - Proteus

T2 - Network-aware Web Browsing on Heterogeneous Mobile Systems

AU - Ren, Jie

AU - Wang, Xiaoming

AU - Fang, Jianbin

AU - Feng, Yansong

AU - Zhu, Dongxiao

AU - Luo, Zhunchen

AU - Wang, Zheng

PY - 2018/12/4

Y1 - 2018/12/4

N2 - 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.

AB - 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.

U2 - 10.1145/3281411.3281422

DO - 10.1145/3281411.3281422

M3 - Conference contribution/Paper

SP - 379

EP - 392

BT - CoNEXT '18 Proceedings of the 14th International Conference on emerging Networking EXperiments and Technologies

PB - ACM

CY - New York

ER -