Collaborative Cloud-Edge-End Task Offloading in Mobile-Edge Computing Networks with Limited Communication Capability
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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If an edge node experiences a failure or disruption, the cloud server can seamlessly take over the processing tasks, which ensures continuous operation and minimizes downtime.
“If an edge node experiences a failure or disruption, the cloud server can seamlessly take over the processing tasks, which ensures continuous operation and minimizes downtime [27].”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [27]
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- Authors
- Kai, C., Zhou, H., Yi, Y., & Huang, W.
- Affiliations
- Hefei University of Technology (CN)
- Venue
- IEEE Transactions on Cognitive Communications and Networking
- Published
- 2021-06
- Volume
- 7
- Issue
- 2
- Pages
- 624-634
- ISSN
- 2332-7731 (ISSN-L)
- 2372-2045
- OpenAlex
- W3080122404
- Cited by
- 249
- References
- 40
- Citations by year
- 2026: 30 · 2025: 50 · 2024: 52 · 2023: 63 · 2022: 38 · 2021: 14 · 2020: 2
- FWCI
- 13.75
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- 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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