Información de la conferencia

GPC 2026: International Conference on Green, Pervasive and Cloud Computing

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Día de Entrega:
2026-08-01
Fecha de Notificación:
2026-09-15
Fecha de conferencia:
2026-12-04
Ubicación:
Nanjing, China
Ediciones:
21
CCF: C   ICORE: C   QUALIS: B3   Vistas: 47404   Seguidores: 18   Asistentes: 12

Solicitud de Artículos

GPC 2026 (International Conference on Green, Pervasive and Cloud Computing) is a CCF C / ICORE C / QUALIS B3 conference held in Nanjing, China on 2026-12-04. The paper submission deadline is 2026-08-01. Acceptance notifications are sent on 2026-09-15.

Introduction Green computing, in the context of cloud and pervasive computing, is an emerging research field in computer science and engineering. Pervasive and cloud computing are promising paradigms that have been seeing exponential growth in the deployments and poised to take major roles in human's daily life. But in order to realize this potential, both are facing the critical challenges related to energy consumption, for example, controlling the energy spent by the large-scale data centers and prolonging the battery life of mobile devices in pervasive computing and Internet of Things (IoT). New concepts and technologies such as mobile cloud computing (MCC), software-defined networking (SDN), Edge / Fog and hybrid cloud services can be explored to make data centers and wireless devices more energy efficient. The 21st International Conference on Green, Pervasive, and Cloud Computing (GPC 2026) focuses on pervasive and environmentally sustainable computing. GPC 2026 will be held on 4th-6th December 2026 at Nanjing, China, and will provide a forum for researchers and practitioners to discuss and exchange novel ideas, results, experiences and work-in-process on Green, Pervasive, and Cloud Computing. Topics And Scope Topics of interest include, but are not limited to: Green and Sustainable Computing and Communication Energy-Efficient Training and Inference for LLMs / Foundation Models Green Multimodal and Generative AI Systems Benchmarking and Evaluation for Green AI Energy-Efficient Processing and Storage Systems Cooling, Thermal-Aware Computing, and Power Capping Computility-Electricity-Carbon Coordination Energy Harvesting Computing and Communication Green Satellite Computing Green Edge Intelligence Efficient Fine-Tuning Under Resource Constraints Distributed Computing Across Cloud-Edge-Device Continuum Model Compression, Pruning, and Quantization Energy-Efficient Edge Inference and On-Device Intelligence Efficient Edge Agents and Lightweight Models Sustainable Federated and Distributed Learning Mobile Sensing and Computing Wireless Sensing, including WiFi, mmWave, RFID, acoustic-based sensing, and related techniques Foundation Models for Mobile and Wearable Sensing Self-Supervised Learning for Multimodal Sensor Data Context-Aware and Personalized Mobile Intelligence On-Device Generative AI and Edge Intelligence for Mobile Systems Urban Computing / Smart City City-scale Spatio-temporal Foundation Models Urban Mobility Intelligence and Human Dynamics Modeling AI-driven Crowdsensing and Participatory Urban Analytics Digital Twin and Edge-enabled Smart City Infrastructure Industrial Digitization and Applications Edge Intelligence and TinyML for Industrial Systems Digital Twin and Cyber-physical Manufacturing Systems Predictive Maintenance with Multimodal Industrial Sensing Federated and Privacy-preserving Learning for Industrial IoT Sustainable and Intelligent Cloud Systems AI-Driven Optimization for Cloud Systems Carbon/Energy-Efficient Cloud Infrastructure Sustainable Resource Management in Cloud Federated and Distributed Cloud Systems Privacy, Security, and Reliability of Cloud AI Services Native Cloud Infrastructure Heterogeneous Resource Management Runtime Management Resource Virtualization and Isolation Energy-Efficient and Sustainable Infrastructure Programming Models for Cloud Cloud-Edge Collaborative Programming Programming in Cloud, such as Serverless and Microservice Distributed Application Programming Workflow Orchestration and Service Composition
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