Evidence-aware Mobile Computational Offloading

Flores, H. and Hui, P. and Nurmi, P. and Lagerspetz, E. and Tarkoma, S. and Manner, J. and Kostakos, V. and Li, Yong and Su, Xiang (2018) Evidence-aware Mobile Computational Offloading. IEEE Transactions on Mobile Computing, 17 (8). pp. 1834-1850. ISSN 1536-1233

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Abstract

Computational offloading can improve user experience of mobile apps through improved responsiveness and reduced energy footprint. Currently, offloading decisions are predominantly based on profiling performed on individual devices. While significant gains have been shown in benchmarks, these gains rarely translate to real-world use due to the complexity of contexts and parameters that affect offloading. We contribute by proposing crowdsensed evidence traces as a novel mechanism for improving the performance of offloading systems. Instead of limiting to profiling individual devices, crowdsensing enables characterising execution contexts across a community of users, providing better generalisation and coverage of contexts. We demonstrate the feasibility of using crowdsensing to characterize offloading contexts through an analysis of two crowdsensing datasets. Motivated by our results, we present the design and development of EMCO toolkit and platform as a novel solution for computational offloading. Experiments carried out on a testbed deployment in Amazon EC2 Ireland demonstrate that EMCO can consistently accelerate app execution while at the same time reduce energy footprint. We demonstrate that EMCO provides better scalability than current cloud platforms, being able to serve a larger number of clients without variations in performance. Our framework, use cases, and tools are available as open source from github.

Item Type: Journal Article
Journal or Publication Title: IEEE Transactions on Mobile Computing
Additional Information: ©2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/2200/2208
Subjects:
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 123746
Deposited By: ep_importer_pure
Deposited On: 16 Mar 2018 14:02
Refereed?: Yes
Published?: Published
Last Modified: 26 Feb 2020 04:18
URI: https://eprints.lancs.ac.uk/id/eprint/123746

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