Smart mobile computation offloading

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dc.contributor.author Tout, Hanine
dc.contributor.author Talhi, Chamseddine
dc.contributor.author Kara, Nadija
dc.contributor.author Mourad, Azzam
dc.date.accessioned 2018-08-14T08:43:00Z
dc.date.available 2018-08-14T08:43:00Z
dc.date.copyright 2017 en_US
dc.date.issued 2018-08-14
dc.identifier.issn 1873-6793 en_US
dc.identifier.uri http://hdl.handle.net/10725/8318
dc.description.abstract Although mobile devices have been considerably upgraded to more powerful terminals, yet their lightness feature still impose intrinsic limitations in their computation capability, storage capacity and battery lifetime. With the ability to release and augment the limited resources of mobile devices, mobile cloud computing has drawn significant research attention allowing computations to be offloaded and executed on remote resourceful infrastructure. Nevertheless, circumstances like mobility, latency, applications execution overload and mobile device state; any can affect the offloading decision, which might dictate local execution for some tasks and remote execution for others. We present in this article a novel system model for computations offloading which goes beyond existing works with smart centralized, selective, and optimized approach. The proposition consists of (1)hotspots selection mechanism to minimize the overhead of the offloading evaluation process yet without jeopardizing the discovery of the optimal processing environment of tasks, (2)a multi-objective optimization model that considers adaptable metrics crucial for minimizing device resource usage and augmenting its performance, and (3)a tailored centralized decision maker that uses genetics to intelligently find the optimal distribution of tasks. The scalability, overhead and performance of the proposed hotspots selection mechanism and hence its effect on the decision maker and tasks dissemination are evaluated. The results show its ability to notably reduce the evaluation cost while the decision maker was able in turn to maintain optimal dissemination of tasks. The model is also evaluated and the experiments prove its competency over existing models with execution speedup and significant reduction in the CPU usage, memory consumption and energy loss. en_US
dc.language.iso en en_US
dc.title Smart mobile computation offloading en_US
dc.type Article en_US
dc.description.version Published en_US
dc.title.subtitle centralized selective and multi-objective approach en_US
dc.author.school SAS en_US
dc.author.idnumber 200904853 en_US
dc.author.department Computer Science and Mathematics en_US
dc.description.embargo N/A en_US
dc.relation.journal Expert Systems with Applications en_US
dc.journal.volume 80 en_US
dc.journal.issue 1 en_US
dc.article.pages 1-13 en_US
dc.keywords Mobile device en_US
dc.keywords Mobile cloud computing en_US
dc.keywords Computation offloading en_US
dc.keywords Selective offloading en_US
dc.keywords Hotspots en_US
dc.keywords Optimization en_US
dc.identifier.doi https://doi.org/10.1016/j.eswa.2017.03.011 en_US
dc.identifier.ctation Tout, H., Talhi, C., Kara, N., & Mourad, A. (2017). Smart mobile computation offloading: Centralized selective and multi-objective approach. Expert Systems with Applications, 80, 1-13. en_US
dc.author.email azzam.mourad@lau.edu.lb en_US
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php en_US
dc.identifier.url https://www.sciencedirect.com/science/article/pii/S0957417417301586 en_US
dc.orcid.id https://orcid.org/0000-0001-9434-5322 en_US
dc.author.affiliation Lebanese American University en_US

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