MPBond: Efficient network-level collaboration among personal mobile devices

Xiao Zhu, Jiachen Sun, Xumiao Zhang, Y. Ethan Guo, Feng Qian, Z. Morley Mao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

MPBond is an efficient system allowing multiple personal mobile devices to collaboratively fetch content from the Internet. For example, a smartwatch can assist its paired smartphone with downloading data. Inspired by the success of MPTCP, MPBond applies the concept of distributed multipath transport where multiple subflows can traverse different devices. We develop a cross-device connection management scheme, a buffering strategy, a packet scheduling algorithm, and a policy framework tailored to MPBond's architecture. We implement MPBond on commodity mobile devices such as Android smartphones and smartwatches. Our real-world evaluations using different workloads under various network conditions demonstrate the efficiency of MPBond. Compared to state-of-the-art collaboration frameworks, MPBond reduces file download time by 5% to 46%, and improves the video streaming bitrate by 2% to 118%. Meanwhile, it improves the energy efficiency by 10% to 57%.

Original languageEnglish (US)
Title of host publicationMobiSys 2020 - Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services
PublisherAssociation for Computing Machinery, Inc
Pages364-376
Number of pages13
ISBN (Electronic)9781450379540
DOIs
StatePublished - Jun 15 2020
Externally publishedYes
Event18th ACM International Conference on Mobile Systems, Applications, and Services, MobiSys 2020 - Toronto, Canada
Duration: Jun 15 2020Jun 19 2020

Publication series

NameMobiSys 2020 - Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services

Conference

Conference18th ACM International Conference on Mobile Systems, Applications, and Services, MobiSys 2020
Country/TerritoryCanada
CityToronto
Period6/15/206/19/20

Bibliographical note

Funding Information:
We would like to thank our shepherd, Aruna Balasubramanian, and the anonymous reviewers for their valuable comments. This work was partially supported by NSF under the grants CNS-1827940, CCF-1628991, CNS-1629763, CNS-1917424, and CCF-1903880.

Publisher Copyright:
© 2020 ACM.

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