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Mobile edge computing (MEC), as a new distributed computing model, satisfies the low energy consumption and low latency requirements of computation-intensive services. The task offloading of MEC has become an important research hotspot, as it solves the problems of insufficient computing capability and battery capacity of Internet of things (IoT) devices. This study investigates task offloading scheduling in a dynamic MEC system. By integrating energy harvesting technology into IoT devices, we propose a hybrid energy supply model. We jointly optimize local computing, offloading duration, and edge computing decisions to minimize system cost. On the basis of stochastic optimization theory, we design an online dynamic task offloading algorithm for MEC with a hybrid energy supply called DTOME. DTOME can make task offloading decisions by weighing system cost and queue stability. We quote dynamic programming theory to obtain the optimal task offloading strategy. Simulation results verify the effectiveness of DTOME, and show that DTOME entails lower system cost than two baseline task offloading strategies.


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Dynamic Task Offloading for Mobile Edge Computing with Hybrid Energy Supply

Show Author's information Ying Chen1( )Fengjun Zhao1Yangguang Lu1Xin Chen1
Computer School, Beijing Information Science and Technology University, Beijing 100101, China

Abstract

Mobile edge computing (MEC), as a new distributed computing model, satisfies the low energy consumption and low latency requirements of computation-intensive services. The task offloading of MEC has become an important research hotspot, as it solves the problems of insufficient computing capability and battery capacity of Internet of things (IoT) devices. This study investigates task offloading scheduling in a dynamic MEC system. By integrating energy harvesting technology into IoT devices, we propose a hybrid energy supply model. We jointly optimize local computing, offloading duration, and edge computing decisions to minimize system cost. On the basis of stochastic optimization theory, we design an online dynamic task offloading algorithm for MEC with a hybrid energy supply called DTOME. DTOME can make task offloading decisions by weighing system cost and queue stability. We quote dynamic programming theory to obtain the optimal task offloading strategy. Simulation results verify the effectiveness of DTOME, and show that DTOME entails lower system cost than two baseline task offloading strategies.

Keywords: mobile edge computing, Internet of Things, stochastic optimization, dynamic offloading, hybrid energy supply

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Publication history

Received: 20 May 2021
Revised: 22 June 2021
Accepted: 12 July 2021
Published: 13 December 2022
Issue date: June 2023

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© The author(s) 2023.

Acknowledgements

This work was partly supported by the National Natural Science Foundation of China (Nos. 61902029 and 61872044), and R&D Program of Beijing Municipal Education Commission (No. KM202011232015).

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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