research-article

MAUI: making smartphones last longer with code offload

Authors:

Eduardo Cuervo,

Aruna Balasubramanian,

Ranveer Chandra,

Paramvir BahlAuthors Info & Claims

MobiSys '10: Proceedings of the 8th international conference on Mobile systems, applications, and services

Pages 49 - 62

https://doi.org/10.1145/1814433.1814441

Published: 15 June 2010 Publication History

Abstract

This paper presents MAUI, a system that enables fine-grained energy-aware offload of mobile code to the infrastructure. Previous approaches to these problems either relied heavily on programmer support to partition an application, or they were coarse-grained requiring full process (or full VM) migration. MAUI uses the benefits of a managed code environment to offer the best of both worlds: it supports fine-grained code offload to maximize energy savings with minimal burden on the programmer. MAUI decides at run-time which methods should be remotely executed, driven by an optimization engine that achieves the best energy savings possible under the mobile device's current connectivity constrains. In our evaluation, we show that MAUI enables: 1) a resource-intensive face recognition application that consumes an order of magnitude less energy, 2) a latency-sensitive arcade game application that doubles its refresh rate, and 3) a voice-based language translation application that bypasses the limitations of the smartphone environment by executing unsupported components remotely.

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Cited By

Zhou ZPan LLiu S(2025)Potential Game-Based Computation Offloading in Edge Computing With Heterogeneous Edge ServersIEEE Transactions on Network Science and Engineering10.1109/TNSE.2024.349454212:1(290-301)Online publication date: Jan-2025
https://doi.org/10.1109/TNSE.2024.3494542
Mondal R(2024)Mobile Cloud ComputingEmerging Trends in Cloud Computing Analytics, Scalability, and Service Models10.4018/979-8-3693-0900-1.ch009(170-185)Online publication date: 22-Mar-2024
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Iyengar RDong QNguyen CPillai PSatyanarayanan M(2024)Offload Shaping for Wearable Cognitive AssistanceElectronics10.3390/electronics1320408313:20(4083)Online publication date: 17-Oct-2024
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MAUI: making smartphones last longer with code offload
1. General and reference
  1. Cross-computing tools and techniques

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Published In

cover image ACM Conferences

MobiSys '10: Proceedings of the 8th international conference on Mobile systems, applications, and services

June 2010

382 pages

ISBN:9781605589855

DOI:10.1145/1814433

General Chair:
Sujata Banerjee
Hewlett-Packard Labs., USA
,
Program Chairs:
Srinivasan Keshav
University of Waterloo, Ontario, Canada
,
Alec Wolman
Microsoft Research Redmond, USA

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing

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SIGOPS: ACM Special Interest Group on Operating Systems

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Association for Computing Machinery

New York, NY, United States

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Published: 15 June 2010

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MobiSys'10

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MobiSys'10: The 8th International Conference on Mobile Systems, Applications, and Services

June 15 - 18, 2010

California, San Francisco, USA

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Cited By

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https://doi.org/10.1109/TNSE.2024.3494542
Mondal R(2024)Mobile Cloud ComputingEmerging Trends in Cloud Computing Analytics, Scalability, and Service Models10.4018/979-8-3693-0900-1.ch009(170-185)Online publication date: 22-Mar-2024
https://doi.org/10.4018/979-8-3693-0900-1.ch009
Iyengar RDong QNguyen CPillai PSatyanarayanan M(2024)Offload Shaping for Wearable Cognitive AssistanceElectronics10.3390/electronics1320408313:20(4083)Online publication date: 17-Oct-2024
https://doi.org/10.3390/electronics13204083
Sun HWang ZYu YSha KWu Y(2024)EdgeBrain: A Game Based Collaborative Computational Task Offloading Framework for Edge Video AnalyticsIEEE Transactions on Services Computing10.1109/TSC.2023.334280017:5(2287-2303)Online publication date: Sep-2024
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