ULOOF: a User Level Online Offloading Framework for Mobile Edge Computing

José L. D. Neto 1, 2 Se-Young Yu 3 Daniel Fernandes Macedo 2 José-Marcos Nogueira 2 Rami Langar 4, 5 Stefano Secci 6, 7
1 Google Inc.
2 DCC - UFMG - Departamento de Ciência da Computação [Minas Gerais]
3 Northwestern University [Evanston]
4 LIGM - Laboratoire d'Informatique Gaspard-Monge
5 UPEM - Université Paris-Est Marne-la-Vallée
6 CNAM - Conservatoire National des Arts et Métiers [CNAM]
7 Phare
LIP6
Abstract : Mobile devices are equipped with limited processing power and battery charge. A mobile computation offloading framework is a software that provides better user experience in terms of computation time and energy consumption, also taking profit from edge computing facilities. This article presents User-Level Online Offloading Framework (ULOOF), a lightweight and efficient framework for mobile computation offloading. ULOOF is equipped with a decision engine that minimizes remote execution overhead, while not requiring any modification in the device's operating system. By means of real experiments with Android systems and simulations using large-scale data from a major cellular network provider, we show that ULOOF can offload up to 73% of computations, and improve the execution time by 50% while at the same time significantly reducing the energy consumption of mobile devices.
Keywords : Machine Learning Computation Offloading Mobile Edge Computing
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Journal articles
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Submitted on : Wednesday, March 14, 2018 - 8:05:19 AM
Last modification on : Thursday, August 8, 2019 - 12:14:05 PM
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José L. D. Neto, Se-Young Yu, Daniel Fernandes Macedo, José-Marcos Nogueira, Rami Langar, et al.. ULOOF: a User Level Online Offloading Framework for Mobile Edge Computing. IEEE Transactions on Mobile Computing, Institute of Electrical and Electronics Engineers, 2018, 17 (11), pp.2660-2674. ⟨10.1109/TMC.2018.2815015⟩. ⟨hal-01547036v2⟩

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