Computing with nearby mobile devices: a work sharing algorithm for mobile edge-clouds

Fernando, Niroshinie, Loke, Seng W. and Rahayu, Wenny 2017, Computing with nearby mobile devices: a work sharing algorithm for mobile edge-clouds, IEEE transactions on cloud computing, pp. 1-14, doi: 10.1109/TCC.2016.2560163.

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Title Computing with nearby mobile devices: a work sharing algorithm for mobile edge-clouds
Author(s) Fernando, Niroshinie
Loke, Seng W.ORCID iD for Loke, Seng W.
Rahayu, Wenny
Journal name IEEE transactions on cloud computing
Start page 1
End page 14
Total pages 14
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2017
ISSN 2168-7161
Keyword(s) mobile edge-clouds
mobile crowd computing
Summary As mobile devices evolve to be powerful and pervasive computingtools, their usage also continues to increase rapidly. However,mobile device users frequently experience problems when running intensiveapplications on the device itself, or offloading to remote clouds,due to resource shortage and connectivity issues. Ironically, most users’environments are saturated with devices with significant computationalresources. This paper argues that nearby mobile devices can efficientlybe utilised as a crowd-powered resource cloud to complement theremote clouds. Node heterogeneity, unknown worker capability, anddynamism are identified as essential challenges to be addressed whenscheduling work among nearby mobile devices. We present a worksharingmodel, called Honeybee, using an adaptation of the well-knownwork stealing method to load balance independent jobs among heterogeneousmobile nodes, able to accommodate nodes randomly leavingand joining the system. The overall strategy of Honeybee is to focus onshort-term goals, taking advantage of opportunities as they arise, basedon the concepts of proactive workers and opportunistic delegator. Weevaluate our model using a prototype framework built using Android andimplement two applications. We report speedups of up to 4 with sevendevices and energy savings up to 71% with eight devices.
Notes In Press
Language eng
DOI 10.1109/TCC.2016.2560163
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, IEEE
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Document type: Journal Article
Collection: School of Information Technology
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