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dc.contributor.advisorBarker, Adam David
dc.contributor.authorSulaiman, Dawand Jalil
dc.coverage.spatialxx, 198 p.en_US
dc.date.accessioned2020-09-14T10:50:18Z
dc.date.available2020-09-14T10:50:18Z
dc.date.issued2020-12-01
dc.identifier.urihttps://hdl.handle.net/10023/20618
dc.description.abstractThe sheer amount of mobile devices and their fast adaptability have contributed to the proliferation of modern advanced mobile applications. These applications have characteristics such as latency-critical and demand high availability. Also, these kinds of applications often require intensive computation resources and excessive energy consumption for processing, a mobile device has limited computation and energy capacity because of the physical size constraints. The heterogeneous mobile cloud environment consists of different computing resources such as remote cloud servers in faraway data centres, cloudlets whose goal is to bring the cloud closer to the users, and nearby mobile devices that can be utilised to offload mobile tasks. Heterogeneity in mobile devices and the different sites include software, hardware, and technology variations. Resource-constrained mobile devices can leverage the shared resource environment to offload their intensive tasks to conserve battery life and improve the overall application performance. However, with such a loosely coupled and mobile device dominating network, new challenges and problems such as how to seamlessly leverage mobile devices with all the offloading sites, how to simplify deploying runtime environment for serving offloading requests from mobile devices, how to identify which parts of the mobile application to offload and how to decide whether to offload them and how to select the most optimal candidate offloading site among others. To overcome the aforementioned challenges, this research work contributes the design and implementation of MAMoC, a loosely coupled end-to-end mobile computation offloading framework. Mobile applications can be adapted to the client library of the framework while the server components are deployed to the offloading sites for serving offloading requests. The evaluation of the offloading decision engine demonstrates the viability of the proposed solution for managing seamless and transparent offloading in distributed and dynamic mobile cloud environments. All the implemented components of this work are publicly available at the following URL: https://github.com/mamoc-reposen_US
dc.language.isoenen_US
dc.publisherUniversity of St Andrews
dc.subjectComputation offloadingen_US
dc.subjectMobile cloud computingen_US
dc.subjectMobile edge computingen_US
dc.subjectMultisite offloadingen_US
dc.subjectAndroid applicationsen_US
dc.subjectMulticriteria decision makingen_US
dc.subject.lccQA76.59S8
dc.subject.lcshMobile computingen
dc.subject.lcshMobile appsen
dc.subject.lcshCloud computingen
dc.subject.lcshEdge computingen
dc.titleMultisite adaptive computation offloading for mobile cloud applicationsen_US
dc.typeThesisen_US
dc.contributor.sponsorUniversity of St Andrews. School of Computer Scienceen_US
dc.type.qualificationlevelDoctoralen_US
dc.type.qualificationnamePhD Doctor of Philosophyen_US
dc.publisher.institutionThe University of St Andrewsen_US
dc.identifier.doihttps://doi.org/10.17630/10023-20618


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