University of Oulu

H. Flores et al., "Evidence-aware Mobile Computational Offloading," in IEEE Transactions on Mobile Computing, vol. PP, no. 99, pp. 1-1. doi: 10.1109/TMC.2017.2777491 keywords: {Acceleration;Cloud computing;Context;Mobile applications;Mobile communication;Mobile computing;Performance evaluation;Big Data;Computational Offloading;Crowdsensing;Mobile Cloud Computing}, URL:

Evidence-aware mobile computational offloading

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Author: Flores, Huber1; Hui, Pan2,3; Nurmi, Petteri2;
Organizations: 1Department of Computer Science, University of Helsinki, Finland
2University of Helsinki, Finland
3Hong Kong University of Science and Technology, Hong Kong
4Aalto University, Finland
5University of Melbourne, Australia
6Tsinghua University, China
7University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 5.7 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2018
Publish Date: 2018-03-06


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.

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Series: IEEE transactions on mobile computing
ISSN: 1536-1233
ISSN-E: 1558-0660
ISSN-L: 1536-1233
Volume: 17
Issue: 8
Pages: 1834 - 1850
DOI: 10.1109/TMC.2017.2777491
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
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