Research Article
Carbon-Computing Coupling Optimization and Green Scheduling System for Intelligent Computing Centers
Guiyuan Xie
,
Wenguo Wei*
Issue:
Volume 9, Issue 2, June 2026
Pages:
49-57
Received:
15 March 2026
Accepted:
27 March 2026
Published:
29 April 2026
Abstract: Under China’s “Dual-Carbon” strategic goal, reducing carbon emissions in computing centers has become a critical challenge. The increasing scale of data centers, particularly in the context of initiatives such as “East Data, West Computing,” necessitates new approaches that jointly optimize computing efficiency and carbon footprint. This paper aims to address this challenge by proposing a novel carbon-computing coupling optimization framework and a green scheduling system designed to minimize the carbon emissions associated with computational tasks while maintaining system robustness. We first establish a carbon efficiency dynamic equilibrium equation and introduce the concept of virtual carbon flow to model the carbon footprint of computing tasks. Based on this modeling, we develop a deep reinforcement learning (DRL) based scheduler that dynamically migrates tasks to low-carbon nodes. In addition, we integrate a digital twin platform that preemptively simulates failure scenarios to enhance system robustness and resilience. Experimental results in simulated “East Data, West Computing” scenarios demonstrate the effectiveness of the proposed approach. The system reduces carbon emissions per unit of computing power by 18%, improves the energy efficiency ratio in western nodes by 35%, and decreases the Mean Time to Recovery (MTTR) from 2 hours to 15 minutes. These findings validate the potential of carbon-computing coupling optimization in achieving both sustainability and reliability goals for large-scale computing centers.
Abstract: Under China’s “Dual-Carbon” strategic goal, reducing carbon emissions in computing centers has become a critical challenge. The increasing scale of data centers, particularly in the context of initiatives such as “East Data, West Computing,” necessitates new approaches that jointly optimize computing efficiency and carbon footprint. This paper aims...
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Review Article
A Conceptual Framework for Load Balancing and Resource Optimization in Cloud Infrastructure Using Intelligent Scheduling Approaches
Issue:
Volume 9, Issue 2, June 2026
Pages:
58-64
Received:
22 April 2026
Accepted:
3 May 2026
Published:
27 May 2026
DOI:
10.11648/j.ajcst.20260902.12
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Views:
Abstract: Cloud computing has fundamentally transformed how computing resources are delivered, offering scalable, on-demand, and cost-efficient services to users across diverse domains. Despite these advantages, the inherently dynamic and heterogeneous characteristics of cloud environments create persistent challenges in achieving efficient workload distribution and optimal resource utilization. When load balancing mechanisms are ineffective, systems experience increased response times, underutilized or overburdened resources, and an overall decline in Quality of Service (QoS), which directly affects user satisfaction and system reliability. To address these issues, this study proposes a comprehensive conceptual framework designed to improve load balancing and resource optimization in cloud infrastructures. The framework emphasizes the integration of multiple components, including advanced task scheduling techniques and dynamic load balancing strategies that can adapt to fluctuating workloads in real time. By leveraging these mechanisms, the system can distribute tasks more evenly across available resources, minimizing bottlenecks and enhancing performance efficiency. A key aspect of the framework is the incorporation of emerging computing paradigms such as fog computing. This approach extends cloud capabilities closer to the data source, thereby reducing latency and improving response times, especially for time-sensitive applications. Additionally, the framework adopts intelligent optimization techniques, combining hybrid metaheuristic algorithms with machine learning models to effectively manage complex and unpredictable workloads. These methods enable the system to learn from past patterns, predict future demands, and make informed decisions regarding resource allocation and task scheduling. The framework also highlights the importance of evaluating system performance using critical metrics such as throughput, response time, makespan, and scalability. These indicators provide a comprehensive understanding of how well the system performs under varying conditions and workloads. By analyzing these metrics, researchers and practitioners can identify areas for improvement and refine the system for better efficiency. Overall, the proposed framework offers a flexible, extensible, and forward-looking foundation for enhancing cloud resource management. It is particularly valuable for guiding future research focused on developing deep learning-driven hybrid optimization models that can further improve adaptability, efficiency, and performance in increasingly complex cloud environments.
Abstract: Cloud computing has fundamentally transformed how computing resources are delivered, offering scalable, on-demand, and cost-efficient services to users across diverse domains. Despite these advantages, the inherently dynamic and heterogeneous characteristics of cloud environments create persistent challenges in achieving efficient workload distribu...
Show More
