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Applied Sciences
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Resource Management for AI-Centric Computing Systems
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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 666

Special Issue Editors

Dr. Kyungwoon Cho

E-Mail Website
Guest Editor
Embedded Software Research Center, Ewha University, Seoul 03760, Republic of Korea
Interests: multimedia systems; cloud computing; real-time systems; embedded systems; operating systems
Special Issues, Collections and Topics in MDPI journals
Dr. Sungyong Ahn

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Pusan National University, Busan 46241, Republic of Korea
Interests: operating system; cloud platform; non-volatile memory storage; non-block based storage
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hyokyung Bahn

E-Mail Website
Guest Editor
Department of Computer Engineering, Ewha University, Seoul 03760, Republic of Korea
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

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 250 words) can be sent to the Editorial Office for assessment.

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. Applied Sciences 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 2400 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.

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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Published Papers (1 paper)

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Research

32 pages, 7135 KB  
Article
Evolutionary Multi-Objective Prompt Learning for Synthetic Text Data Generation with Black-Box Large Language Models
by Diego Pastrián, Nicolás Hidalgo, Víctor Reyes and Erika Rosas
Appl. Sci. 2026, 16(8), 3623; https://doi.org/10.3390/app16083623 - 8 Apr 2026
Viewed by 431
Abstract
High-quality training data are essential for the performance and generalization of artificial intelligence systems, particularly in dynamic environments such as adaptive stream processing for disaster response. However, constructing large and representative datasets remains costly and time-consuming, especially in domains where real data are [...] Read more.
High-quality training data are essential for the performance and generalization of artificial intelligence systems, particularly in dynamic environments such as adaptive stream processing for disaster response. However, constructing large and representative datasets remains costly and time-consuming, especially in domains where real data are scarce or difficult to obtain. Large Language Models (LLMs) provide powerful capabilities for synthetic text generation, yet the quality of generated data strongly depends on the design of input prompts. Prompt engineering is therefore critical, but it remains largely manual and difficult to scale, particularly in black-box settings where model internals are inaccessible. This work introduces EVOLMD-MO, a multi-objective evolutionary framework for automated prompt learning aimed at generating high-quality synthetic text datasets using black-box LLMs. The proposed approach formulates prompt optimization as a multi-objective search problem in which candidate prompts evolve through genetic operators guided by two complementary objectives: semantic fidelity to reference data and generative diversity of the produced samples. To support scalable optimization, the framework integrates a modular multi-agent architecture that decouples prompt evolution, LLM interaction, and evaluation mechanisms. The evolutionary process is implemented using the NSGA-II algorithm, enabling the discovery of diverse Pareto-optimal prompts that balance semantic preservation and diversity. Experimental evaluation using large-scale disaster-related social media data demonstrates that the proposed approach consistently improves prompt quality across generations while maintaining a stable trade-off between fidelity and diversity. Compared with a single-objective baseline, EVOLMD-MO explores a significantly broader semantic search space and produces more diverse yet semantically coherent synthetic datasets. These results indicate that multi-objective evolutionary prompt learning constitutes a promising strategy for black-box LLM-driven data generation, with potential applicability to adaptive data analytics and real-time decision-support systems in highly dynamic environments, pending broader validation across domains and models. Full article
(This article belongs to the Special Issue Resource Management for AI-Centric Computing Systems)
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