Advanced Computational Intelligence in Cloud/Edge Computing
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".
Deadline for manuscript submissions: 1 March 2025 | Viewed by 2828
Special Issue Editors
Interests: cloud/edge computing; resource optimization; machine learning
Interests: federated learning; mobile edge computing; cyber security; AI security
Interests: mobile edge computing; software-defined networking; network function virtualization; AI/ML-driven resource optimization; performance modeling and analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Integrating AI and cloud/edge computing fully unleashes their potential values, leading to a new intelligence computing paradigm. However, there are still many open-ended challenges during its implementation, such as limited computing, networks, and energy resources, accompanied by serious security issues. Meanwhile, the dynamic features of cloud/edge environments also complicate matters. Under this landscape, computational intelligence has emerged that focuses on crafting diverse computational techniques inspired by intelligent behaviors in nature and biology. By learning from data and making decisions grounded in discernment, among other methods, such techniques can lead to machines with the capabilities to solve complicated problems. Therefore, advanced computational intelligence exhibits great promise and abundant prospects for applications in cloud/edge computing, which can serve as both an enabler that bolsters the service capabilities and as a problem-solver, surmounting the obstacles during the system design. This Special Issue endeavors to assemble scholarly studies that explore the paths of synergizing cloud/edge computing with advanced computational intelligence to guide the development of next-generation network technology.
The topics of this Special Issue include but are not limited to the following:
- Uncertainty-aware intelligence in dynamic cloud/edge environments;
- Computing, networks, and energy optimization for cloud/edge intelligence applications;
- Advanced deep reinforcement learning for cloud/edge computing;
- Novel task scheduling and offloading methods in cloud/edge computing;
- Novel service deployment and migration methods in cloud/edge computing;
- Advanced federated learning for cloud/edge computing;
- Novel traffic prediction and content-caching methods in cloud/edge computing;
- Cost-aware federated learning in cloud/edge computing;
- Security and privacy protection for cloud/edge intelligence applications;
- Model compression for efficient training and inference in cloud/edge computing.
Prof. Dr. Zheyi Chen
Dr. Zhengxin Yu
Dr. Wang Miao
Guest Editors
Manuscript Submission Information
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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. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.
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Keywords
- cloud/edge computing
- resource optimization
- deep reinforcement learning
- task scheduling/offloading
- service deployment/migration
- federated learning
- traffic prediction
- content caching
- security/privacy protection
- model compression
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