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SNARF: a social networking-inspired accelerator remoting framework

Published: 17 August 2012 Publication History

Abstract

The diminishing size and battery requirements of mobile devices restrict the scope of computations possible on such devices and motivate approaches that support the selective offloading of computations to remote resources. With a variety of resources available to potentially host offloaded computations -- such as cloud-provisioned resources, and devices within a user's personal or social network -- a key challenge lies in architecting a framework that enables applications to seamlessly discover available services, effectively and securely communicate with them, and be presented with API interfaces that hide the complexities associated with managing the interactions with a remote device from applications and present the abstraction of a local device. In this paper, we outline a framework that addresses these challenges by layering APIs and an offload infrastructure upon a virtual networking substrate that supports TCP/IP networking and widely-used resource discovery protocols. An intelligent runtime scheduling layer monitors the execution environment and provides opportunistic remote offloads based on the performance requirements, offload benefits and expendable power. We demonstrate the feasibility of the approach through experiments that evaluate end-to-end application execution times and energy consumption in offloaded mobile devices, as well as the ability to support universal plug-and-play (UPnP) resource discovery in both local- and wide-area environments.

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Cited By

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  • (2016)Benchmark applications used in mobile cloud computing researchThe Journal of Supercomputing10.1007/s11227-016-1674-272:4(1431-1452)Online publication date: 1-Apr-2016
  • (2015)A survey of computation offloading strategies for performance improvement of applications running on mobile devicesJournal of Network and Computer Applications10.1016/j.jnca.2015.05.01856:C(28-40)Online publication date: 1-Oct-2015
  • (2013)Machine Learning-Based Runtime Scheduler for Mobile Offloading FrameworkProceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing10.1109/UCC.2013.21(17-25)Online publication date: 9-Dec-2013
  • Show More Cited By

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Published In

cover image ACM Conferences
MCC '12: Proceedings of the first edition of the MCC workshop on Mobile cloud computing
August 2012
72 pages
ISBN:9781450315197
DOI:10.1145/2342509
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 August 2012

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Author Tags

  1. energy consumption
  2. mobile device
  3. offload
  4. virtual network

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  • Research-article

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SIGCOMM '12
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SIGCOMM '12: ACM SIGCOMM 2012 Conference
August 17, 2012
Helsinki, Finland

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Overall Acceptance Rate 10 of 24 submissions, 42%

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Cited By

View all
  • (2016)Benchmark applications used in mobile cloud computing researchThe Journal of Supercomputing10.1007/s11227-016-1674-272:4(1431-1452)Online publication date: 1-Apr-2016
  • (2015)A survey of computation offloading strategies for performance improvement of applications running on mobile devicesJournal of Network and Computer Applications10.1016/j.jnca.2015.05.01856:C(28-40)Online publication date: 1-Oct-2015
  • (2013)Machine Learning-Based Runtime Scheduler for Mobile Offloading FrameworkProceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing10.1109/UCC.2013.21(17-25)Online publication date: 9-Dec-2013
  • (2013)Collaborative Computing On-demandProceedings of the 2013 IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems10.1109/MASS.2013.23(342-350)Online publication date: 14-Oct-2013
  • (2013)Extending the capabilities of mobile devices for online social applications through cloud offloadingProceedings of the 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing10.1109/CCGrid.2013.52(160-163)Online publication date: 13-May-2013

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