Cited by 249IoT and Edge/Fog ComputingAdvanced Wireless Network OptimizationEnergy Efficiency in Computing

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Collaborative Cloud-Edge-End Task Offloading in Mobile-Edge Computing Networks With Limited Communication Capability

Kai, Caihong, Zhou, Hao, Yi, Yibo, Huang, Wei·2021·IEEE Transactions on Cognitive Communications and Networking

DOI 10.1109/tccn.2020.3018159

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Abstract

Mobile edge computing (MEC) is an emerging computing paradigm for enabling low-latency, high-bandwidth and agile mobile services by deploying computing platform at the edge of network. In order to improve the cloud-edge-end processing efficiency of the tasks within the limited computation and communication capabilities, in this article, we investigate the collaborative computation offloading, computation and communication resource allocation scheme, and develop a collaborative computing framework that the tasks of mobile devices (MDs) can be partially processed at the terminals, edge nodes (EN) and cloud center (CC). Then, we propose the pipeline-based offloading scheme, where both MDs and ENs can offload computation-intensive tasks to a particular EN and CC, according to their computation and communication capacities, respectively. Based on the proposed pipeline offloading strategy, a sum latency of all MDs minimization problem is formulated with the consideration of the offloading strategy, computation resource, delivery rate and power allocation, which is a non-convex problem and difficult to deal with. To solve the optimization problem, by using the classic successive convex approximation (SCA) approach, we transform the non-convex optimization problem into the convex one. Finally, simulation results indicate that the proposed collaboration offloading scheme with the pipeline strategy is efficient and outperforms other offloading schemes.

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Authors: Kai, C., Zhou, H., Yi, Y., & Huang, W.
Affiliations: Hefei University of Technology (CN)
ORCID: Kai, Caihong
Zhou, Hao
Yi, Yibo
Huang, Wei
Venue: IEEE Transactions on Cognitive Communications and Networking
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2021-06
Volume: 7
Issue: 2
Pages: 624-634
ISSN: 2332-7731 (ISSN-L)
2372-2045
DOI: 10.1109/tccn.2020.3018159
OpenAlex: W3080122404
Cited by: 249
References: 40
Citations by year: 2026: 30 · 2025: 50 · 2024: 52 · 2023: 63 · 2022: 38 · 2021: 14
FWCI: 13.75
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Transactions on Cognitive Communications and Networking (publishedVersion)
Funders: National Natural Science Foundation of China (61971176, 61901156)
Anhui Provincial Natural Science Foundation (2008085QF281)
Fundamental Research Funds for the Central Universities of China (PA2020GDKC0008, JZ2019HGBZ0144)
Topics: Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Network Optimization
Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
Keywords: mobile edge computing, task offloading, cloud-edge-end collaboration, latency reduction, power allocation, successive convex approximation, non-convex optimization, mobile devices

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Collaborative Cloud-Edge-End Task Offloading in Mobile-Edge Computing Networks With Limited Communication Capability

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