Evidence-aware Mobile Computational Offloading

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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IEEE Transactions on Mobile Computing
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.
Acceleration, Big Data, Cloud computing, Computational Offloading, Context, Crowdsensing, Mobile applications, Mobile Cloud Computing, Mobile communication, Mobile computing, Performance evaluation
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Flores, H, Hui, P, Nurmi, P, Lagerspetz, E, Tarkoma, S, Manner, J, Kostakos, V, Li, Y & Su, X 2018, ' Evidence-aware Mobile Computational Offloading ', IEEE Transactions on Mobile Computing, vol. 17, no. 8, pp. 1834-1850 . https://doi.org/10.1109/TMC.2017.2777491