Collaboration of Cloud and Edge Computing and Application
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 October 2026 | Viewed by 9
Special Issue Editor
Special Issue Information
Dear Colleagues,
Cloud computing is a widely used computing paradigm that can provide end users with unlimited computing resources anytime and anywhere according to their needs, and users only need to pay for the services they use. Driven by the unimaginable computing power and the scalability of cloud computing, it has become one of the hottest topics in academia and industry. However, the explosive growth of mobile devices and wearable devices (the most numerous consumer electronics), has resulted in massive heterogeneous data. Traditional cloud computing architecture is no longer applicable for real-time-sensitive applications. Edge computing was proposed to solve these kinds of problems. However, edge nodes usually have limited computing power and energy. Hence, uploading part of the data to the cloud for further processing is one of the most intuitive approaches to overcoming this problem, which has made cloud–edge collaboration one of the hottest topics.
The goal of the Special Issue is to solicit high-quality original papers aiming at demonstrate effective and efficient cloud–edge collaboration computing paradigms and optimization techniques in data analysis, resource allocation, privacy preservation, architecture design, etc.
This Special Issue solicits, but is not limited to, the following topics:
- Computing paradigm for cloud-edge collaboration;
- Communication algorithm for cloud-edge collaboration;
- Graph algorithms for cloud-edge collaboration;
- Smart chips for cloud-edge collaboration;
- Efficient network architecture;
- Privacy-primary collaboration;
- Edge pre-trained models;
- Edge graph neural networks;
- Edge–cloud reinforcement learning;
- AI computing hardware in edge computing/ cloud computing;
- CNN/DNN/GNN for cloud-edge collaboration;
- Reinforcement learning for cloud-edge collaboration;
- Federated reinforcement learning for cloud-edge collaboration.
Dr. Zhigao Zheng
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- cloud-edge collaboration
- graph neural networks
- high-performance computing
- resource allocation
- energy management
- reinforcement learning
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