CLOSED: Call for Papers: Special Issue on Generative AI and Large Vision and Language Models in Service Computing

Service computing has revolutionized how modern systems operate by enabling scalable, flexible, and intelligent solutions in diverse domains such as cloud computing, edge frameworks, and IoT platforms. However, as services grow in complexity and scale, challenges arise in resource allocation, adaptive workflows, and real-time decision-making. Generative AI (GenAI) and large models, including vision, language, and multimodal (vision-language) models, offer groundbreaking solutions to these challenges. Specifically, GenAI models like generative adversarial networks (GANs), diffusion models, and variational autoencoders (VAEs) excel at creating synthetic data and enhancing proactive decision-making. Additionally, large vision models are essential for tasks such as image recognition and video analysis in healthcare, surveillance, and smart cities. Moreover, large language models (LLMs) innovate natural language understanding and conversational AI, while large vision-language models (LVLMs) facilitate intelligent content generation and cross-modal reasoning. While these technologies present immense opportunities, challenges such as scalability, heterogeneity, privacy, security, and fairness remain critical areas of research. Accordingly, this special issue aims to address these issues by fostering interdisciplinary efforts at the intersection of GenAI, large models, and service computing. We seek original and high-quality submissions for innovative applications and methodologies tailored to service computing, including but not limited to:

• IoT-integrated GenAI and large model services
• Use cases of virtual-real-world GenAI and large model service applications
• Fault detection and recovery for GenAI and large model services
• Performance monitoring and analytics of GenAI and large model services
• Blockchain-driven GenAI and large model services
• GenAI for workflow automation and adaptive service delivery
• Synthetic data generation for robust training in service applications
• Resource optimization and cost-efficient service management using GenAI models
• Vision models for real-time object detection and video analytics in service environments
• Applications in domain-specific services such as healthcare, retail, and security
• Advanced LLMs for conversational AI, service analytics, and text summarization
• Integration of LLMs into intelligent virtual assistants and knowledge management systems
• Vision-language models for multimodal reasoning and cross-domain service applications
• Innovations in content generation and intelligent task automation using LVLMs
• Pre-training, fine-tuning, and transfer learning strategies for large models in service ecosystems
• Techniques for compressing and optimizing large models for efficient deployment
• Edge computing strategies for deploying large models in real-time service settings
• Ensuring fairness, security, and privacy in generative and large model-based services
• Enhancing interpretability and transparency of model outputs in critical applications
• Metrics and benchmarks for evaluating the robustness, usability, and scalability of large models in service computing
• Distributed computing frameworks for managing large model integration in dynamic environments
• Comparative assessments of GenAI, vision, language, and multimodal models in service tasks
• Human-AI collaboration in creative and service delivery workflows powered by large models
• Applications of large models in improving Quality of Service (QoS) and Quality of Experience (QoE)
• Emerging paradigms for integrating GenAI and large models in next-generation services

Guest Editors

• Professor Dusit Niyato, Nanyang Technological University, Singapore
• Professor Geng Sun, Jilin University, China
• Professor Thomas Tie Luo, University of Kentucky, USA

Paper Submission

For author information and guidelines on submission criteria, please visit Author Resources . Please submit papers through the IEEE Author Portal system and be sure to select the special-issue name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts. Every manuscript should be no more than 14 pages. Submitted manuscripts should not have been previously published nor be currently under review for publication elsewhere. Moreover, they should a provide minimum of 30% original technical contributions in comparison to previous publications.