Advanced Computational Intelligence in Cloud/Edge Computing

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

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 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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.

• 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