AI-Driven Co-Design Strategies for Networking and Storage Systems

Dear Colleagues,

It is our pleasure to announce the launch of a Special Issue entitled "AI-Driven Co-Design Strategies for Networking and Storage Systems". This Special Issue aims to present research related to networking and storage systems through AI-driven co-design, and thus welcomes submissions that address architectures, algorithms, and frameworks that leverage artificial intelligence to jointly optimize data movement, computation, and storage across the stack. The Special Issue will also explore relevant architectures, operating systems, management software, and optimization algorithms. We believe that the findings presented will provide a foundational reference for the networking and storage communities across academia and industry. The scope of this Special Issue includes, but is not limited to, the following topics:

• AI-optimized network transport for storage I/O stacks;
• Neural network-guided data placement and tiering;
• Reinforcement learning for storage-aware network scheduling;
• Unified AI control planes for networked storage;
• In-network computing for AI-storage acceleration;
• Software-defined storage/networking with AI orchestration;
• Low-latency storage access via RDMA/Compute Express Link (CXL) fabrics;
• Energy-efficient joint optimization of network and storage;
• Network-attached computational storage for edge AI;
• Federated learning architectures for distributed storage resilience;
• AI-oriented protocol co-design (NVMe-oF, S3, etc.);
• Generative AI for predictive storage-network resource allocation.

Moreover, this Special Issue welcomes the discussion of new ideas, in addition to the aforementioned topics.

We look forward to receiving your contributions.

Dr. Congming Gao
Dr. Xin Xin
Prof. Dr. Qiao Xiang
Guest Editors

Manuscript Submission Information

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