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Mobility- and Energy-Aware Cooperative Edge Offloading for Dependent Computation Tasks †

1
Deutsche Telekom Chair of Communication Networks, Technische Universität Dresden, 01069 Dresden, Germany
2
Huawei Technologies Sweden AB, Skalholtsgatan 9, 164 40 Kista, Sweden
3
School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ 85287-5706, USA
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in the Proceedings of the IEEE Globecom 2020 conference.
Academic Editor: Amitava Datta
Network 2021, 1(2), 191-214; https://doi.org/10.3390/network1020012
Received: 20 July 2021 / Revised: 26 August 2021 / Accepted: 1 September 2021 / Published: 4 September 2021
(This article belongs to the Special Issue Network Slicing)
Cooperative edge offloading to nearby end devices via Device-to-Device (D2D) links in edge networks with sliced computing resources has mainly been studied for end devices (helper nodes) that are stationary (or follow predetermined mobility paths) and for independent computation tasks. However, end devices are often mobile, and a given application request commonly requires a set of dependent computation tasks. We formulate a novel model for the cooperative edge offloading of dependent computation tasks to mobile helper nodes. We model the task dependencies with a general task dependency graph. Our model employs the state-of-the-art deep-learning-based PECNet mobility model and offloads a task only when the sojourn time in the coverage area of a helper node or Multi-access Edge Computing (MEC) server is sufficiently long. We formulate the minimization problem for the consumed battery energy for task execution, task data transmission, and waiting for offloaded task results on end devices. We convert the resulting non-convex mixed integer nonlinear programming problem into an equivalent quadratically constrained quadratic programming (QCQP) problem, which we solve via a novel Energy-Efficient Task Offloading (EETO) algorithm. The numerical evaluations indicate that the EETO approach consistently reduces the battery energy consumption across a wide range of task complexities and task completion deadlines and can thus extend the battery lifetimes of mobile devices operating with sliced edge computing resources. View Full-Text
Keywords: battery energy; device-enhanced edge computing; device-to-device (D2D) communication; mobility; multi-access edge computing (MEC); sliced edge computing; task dependencies; task offloading battery energy; device-enhanced edge computing; device-to-device (D2D) communication; mobility; multi-access edge computing (MEC); sliced edge computing; task dependencies; task offloading
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MDPI and ACS Style

Mehrabi, M.; Shen, S.; Hai, Y.; Latzko, V.; Koudouridis, G.P.; Gelabert, X.; Reisslein, M.; Fitzek, F.H.P. Mobility- and Energy-Aware Cooperative Edge Offloading for Dependent Computation Tasks. Network 2021, 1, 191-214. https://doi.org/10.3390/network1020012

AMA Style

Mehrabi M, Shen S, Hai Y, Latzko V, Koudouridis GP, Gelabert X, Reisslein M, Fitzek FHP. Mobility- and Energy-Aware Cooperative Edge Offloading for Dependent Computation Tasks. Network. 2021; 1(2):191-214. https://doi.org/10.3390/network1020012

Chicago/Turabian Style

Mehrabi, Mahshid, Shiwei Shen, Yilun Hai, Vincent Latzko, George P. Koudouridis, Xavier Gelabert, Martin Reisslein, and Frank H.P. Fitzek. 2021. "Mobility- and Energy-Aware Cooperative Edge Offloading for Dependent Computation Tasks" Network 1, no. 2: 191-214. https://doi.org/10.3390/network1020012

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