Resource Management for AI-Centric Computing Systems
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 31 August 2026 | Viewed by 19
Special Issue Editors
Interests: multimedia systems; cloud computing; real-time systems; embedded systems; operating systems
Special Issues, Collections and Topics in MDPI journals
Interests: operating system; cloud platform; non-volatile memory storage; non-block based storage
Special Issues, Collections and Topics in MDPI journals
Interests: operating system; real-time system; memory & storage management; embedded systems; system optimizations
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
AI-centric computing systems are evolving in fundamentally different directions from traditional architectures to meet the significant demands of modern artificial intelligence. From a hardware perspective, these systems go beyond general-purpose computing and increasingly adopt heterogeneous accelerators—such as GPGPUs, Neural Processing Units (NPUs), and TPUs—along with high-performance storage hierarchies, including NVMe SSDs and Persistent Memory, to handle the massive volumes of data and parameters required for deep learning. Moreover, AI-centric systems no longer operate in isolation; instead, they dynamically leverage distributed resources across cloud data centers and edge devices through offloading and model partitioning techniques.
On the software side, the proliferation of AI (artificial intelligence) and ML (machine learning) workloads—ranging from Large Language Models (LLMs) to autonomous driving perception systems—has fundamentally changed patterns of system resource utilization. AI-centric applications demand large memory capacities to store model parameters and intermediate tensors (e.g., KV caches) and have strict time constraints for real-time inference. Crucially, the data access patterns in these workloads often lack strong temporal or spatial locality, rendering traditional resource management strategies (such as conventional LRU caching) ineffective.
This Special Issue aims to address these challenges by exploring new resource management paradigms that reflect the unique characteristics of AI workloads and the specialized hardware of AI-centric systems. We invite submissions that propose resource management techniques suitable for AI-centric computing environments. Topics of interest include, but are not limited to, the following.
- AI-aware Resource Scheduling: Scheduling for heterogeneous accelerators (GPU/NPU) and distributed clusters.
- Memory and Storage Optimization for AI: Management of massive embedding tables and KV caches for emerging memory and storage technologies (HBM/DRAM/PM/SSD).
- Energy-Efficient AI Computing: DVFS and power management, specifically for training and inference phases.
- Edge-Cloud Collaboration: Task offloading, model splitting, and migration for distributed AI inference.
- Real-time AI Resource Management: Guaranteeing QoS for latency-critical applications like autonomous driving and robotics.
Dr. Kyungwoon Cho
Dr. Sungyong Ahn
Prof. Dr. Hyokyung Bahn
Guest Editors
Manuscript Submission Information
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Keywords
- AI-centric system
- machine learning
- resource management
- storage management
- memory management
- cloud resource management
- real-time embedded systems
- caching
- scheduling
- energy-saving technique
- task offloading
- dynamic voltage/frequency scailing
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