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. Attached Files Name Description MIMEType Size Downloads Title Computing with nearby mobile devices: a work sharing algorithm for mobile edge-clouds Author(s) Fernando, Niroshinie Loke, Seng W. orcid.org/0000-0001-9568-5230 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 crowdsourcing mobile crowd computing offloading 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 Persistent URL http://hdl.handle.net/10536/DRO/DU:30088296 Document type: Journal Article Collection: School of Information Technology Related Links Link Description Connect to in press version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Fri, 28 Oct 2016, 09:43:14 EST Sat, 29 Oct 2016, 04:00:35 EST Tue, 01 Nov 2016, 11:59:42 EST Fri, 03 Nov 2017, 15:26:01 EST Sat, 04 Nov 2017, 05:01:11 EST Fri, 10 Nov 2017, 08:22:29 EST Fri, 10 Nov 2017, 08:25:40 EST Tue, 17 Jul 2018, 16:04:16 EST Mon, 22 Oct 2018, 08:20:23 EST Mon, 22 Oct 2018, 08:48:03 EST Tue, 19 Feb 2019, 13:05:08 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 169 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Fri, 03 Nov 2017, 15:26:01 EST

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