Session 10: Optimal Allocation of AI Resources and Collaborative Scheduling of Computing and Power
“人工智能资源优化配置与算电协同调度”
At a juncture where artificial intelligence (AI) technologies are experiencing ubiquitous adoption and industry-wide development, the efficient utilization and sustainable development of intelligent resources have emerged as critical issues. This session serves as a convergence point for multi-disciplinary integration across data resources, computational resources, and energy resources, aiming to explore current resource consumption patterns in AI applications, analyze hardware infrastructure development trends, and formulate energy security strategies.
By assembling leading industry experts, academic scholars, and corporate representatives, this session will conduct in-depth analyses of energy efficiency challenges in data centers, investigate implementation pathways for green computing power, drive technological innovation in AI under resource constraints, and ultimately facilitate the construction of a low-carbon, highly efficient, and sustainable intelligent society.
在人工智能技术普及发展的当下，智能资源的高效利用与可持续发展成为关键议题。本次论坛深度融合数据资源、计算资源、能源资源等多领域，探讨人工智能应用中的资源消耗现状、硬件设施发展趋势及能源保障策略。论坛将汇聚行业专家、学者及企业代表，共同剖析数据中心节能难题，探索绿色算力的实现路径，推动人工智能技术在资源约束下的创新发展，助力构建低碳、高效、可持续的智能社会。
Topics (Including but not limited to)
- 1. Analysis of AI Training Requirements and Optimization Strategies: This topic explores the consumption patterns of data, computational, and energy resources during the model construction process, along with optimization methods.
人工智能训练需求分析与优化策略：探讨模型构建过程中数据、计算、能源资源的消耗模式及优化方法。 - 2. Lightweight Deployment of AI Models and Resource Conservation: This discussion focuses on reducing resource consumption during model deployment and application through techniques such as compression and quantization.
人工智能模型轻量化部署与资源节约：探讨通过压缩、量化等技术降低模型部署应用过程中的资源消耗。 - 3. Collaborative Allocation of Computing Power Across Multiple Data Centers and Energy Efficiency Enhancement: This session analyzes the impact of hardware and software platforms on data center energy efficiency and explores mechanisms for collaborative computing power scheduling.
多数据中心算力协同分配与能效提升：分析软硬件平台对数据中心能效的影响，探索算力协同调度机制。 - 4. Energy Security and Collaborative Scheduling Mechanisms for Computing and Power: This research investigates the collaborative optimization of renewable energy supply and AI computing demands to improve overall energy utilization efficiency.
能源保障与算电协同调度机制：研究可再生能源供应与AI计算需求的协同优化，提升能源综合利用率。 - 5. Energy-Saving Technologies in Data Centers and Practices in Green Computing Power:
This presentation shares case studies on energy-saving technologies in data centers and promotes the development of green computing power.
数据中心节能技术与绿色算力实践：分享数据中心节能技术案例，推动绿色算力发展。
Chair: Assoc. Prof. Haoran Li, Shandong University of Finance and Economics, China
Li Haoran is an associate professor at Shandong University of Finance and Economics and Shandong Provincial Key Laboratory of Blockchain Finance. After obtaining his Ph.D. in Control Theory and Control Engineering from Shandong University in 2022, he completed his postdoctoral research at the School of Electrical Engineering in 2024. His research interest is energy system of smart cities, specifically encompassing urban energy system analysis and optimization, the transformation of energy data into factors of production, carbon emission accounting and trajectory analysis, as well as energy market economic policies.
Critical Dates/重要日期
| Submission of Full Paper: | September 15th, 2025 |
| 投稿截止日： | 2025年9月15日 |
| Notification Deadline | September 30th, 2025 |
| 通知书发送： | 2025年9月30日 |
| Registration Deadline: | October 15th, 2025 |
| 注册截止日： | 2025年10月15日 |
Submission Guideline / 投稿指南
Template Download: Formatting.doc (文章模板)
Submit Now: Electronic Submission System (投稿链接)
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2025 the 9th International Conference on Smart Grid and Smart Cities (ICSGSC)