Resource Coordination and Joint Optimization in Cloud-Edge-End Systems

With the rapid advancements in machine learning, cloud computing, edge computing, and terminal devices, cloud-edge-end (CEE) collaboration has become a significant research frontier in the field of information technology. The resource discrepancies in computation, storage, and network bandwidth between the cloud, edge, and terminal layers have made efficient resource scheduling and optimization a core challenge in practical applications. Specifically, the ability to achieve seamless resource coordination and joint optimization across these layers in dynamic environments, while addressing challenges such as real-time requirements, bandwidth constraints, latency, and data security, has become a key research issue.

This special issue aims to explore innovative solutions for efficient resource scheduling and joint optimization in cloud, edge, and terminal environments. We invite contributions that investigate resource coordination, computational and network optimization, distributed system designs, data security, and practical applications in the context of cloud-edge-end systems.

The goal of this special issue is to gather experts, scholars, and industry professionals from academia, industry, and government to discuss new technologies, methodologies, and applications related to resource coordination and joint optimization in cloud-edge-end systems. Specifically, the objectives are as follows:

- Energy consumption modeling, evaluation, and calculation in cloud-edge-end systems

- Dynamic allocation and scheduling algorithms for computational resources

- Federated learning algorithm in cloud-edge-end systems

- Privacy and security in cloud-edge-end systems

- Task offloading and load balancing in cloud-edge-end systems

- AI-based resource scheduling optimization in cloud-edge-end systems

- Intelligent model/algorithm in cloud-edge-end systems

- Network architecture design and optimization for cloud-edge-end systems

- Data transmission and caching mechanisms optimization for cloud-edge-end systems

- Distributed resource management and joint optimization algorithms for cloud-edge-end systems

- Consistency and fault tolerance issues in cloud-edge-end coordination

- Multi-layer, multi-objective joint optimization models for cloud-edge-end systems

- Intelligent sensing and data analysis in cloud-edge-end systems

- Implementation of self-organizing and self-optimizing mechanisms for cloud-edge-end systems

- Cloud-edge-end coordination in intelligent transportation systems

- Resource optimization in IoT and smart manufacturing

- Collaborative optimization solutions for 5G/6G and future networks

Keywords: Cloud-Edge-End, Resource Scheduling, Joint Optimization, Distributed Systems, Task Offloading, Edge Computing, Federated Learning, AI-based Optimization, Data Security and Privacy, Network Architecture

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.