本文围绕数据中心数据汇聚中对动态可扩展性和智能调度的高度需求,提出并实现了一套创新的数据汇聚系统。通过集成实时数据流量与网络负载监测、ARIMA模型进行精准预测以及基于动态规划的资源调度算法,系统能够实时响应数据处理需求的变...本文围绕数据中心数据汇聚中对动态可扩展性和智能调度的高度需求,提出并实现了一套创新的数据汇聚系统。通过集成实时数据流量与网络负载监测、ARIMA模型进行精准预测以及基于动态规划的资源调度算法,系统能够实时响应数据处理需求的变化,自动优化数据汇聚节点和网络带宽配置。相比于传统的静态配置系统,本系统在理论上展示了在数据处理效率、系统稳定性、成本节约方面的潜在优势,尤其是在应对突发数据流量和长期数据处理需求波动方面展现出高度的灵活性与效率。研究成果不仅为数据中心的智能化管理和动态资源分配提供了新思路,还对促进数据中心行业的可持续发展具有重要价值。This article proposes and implements an innovative data aggregation system based on the high demand for dynamic scalability and intelligent scheduling in data center data aggregation. By integrating real-time data traffic and network load monitoring, ARIMA models for accurate prediction, and dynamic programming based resource scheduling algorithms, the system can respond in real-time to changes in data processing requirements, automatically optimize data aggregation nodes and network bandwidth configuration. Compared to traditional static configuration systems, this system theoretically demonstrates potential advantages in data processing efficiency, system stability, and cost savings, especially in dealing with sudden data traffic and long-term data processing demand fluctuations, demonstrating high flexibility and efficiency. The research results not only provide new ideas for the intelligent management and dynamic resource allocation of data centers, but also have important value in promoting the sustainable development of the data center industry.展开更多
文摘本文围绕数据中心数据汇聚中对动态可扩展性和智能调度的高度需求,提出并实现了一套创新的数据汇聚系统。通过集成实时数据流量与网络负载监测、ARIMA模型进行精准预测以及基于动态规划的资源调度算法,系统能够实时响应数据处理需求的变化,自动优化数据汇聚节点和网络带宽配置。相比于传统的静态配置系统,本系统在理论上展示了在数据处理效率、系统稳定性、成本节约方面的潜在优势,尤其是在应对突发数据流量和长期数据处理需求波动方面展现出高度的灵活性与效率。研究成果不仅为数据中心的智能化管理和动态资源分配提供了新思路,还对促进数据中心行业的可持续发展具有重要价值。This article proposes and implements an innovative data aggregation system based on the high demand for dynamic scalability and intelligent scheduling in data center data aggregation. By integrating real-time data traffic and network load monitoring, ARIMA models for accurate prediction, and dynamic programming based resource scheduling algorithms, the system can respond in real-time to changes in data processing requirements, automatically optimize data aggregation nodes and network bandwidth configuration. Compared to traditional static configuration systems, this system theoretically demonstrates potential advantages in data processing efficiency, system stability, and cost savings, especially in dealing with sudden data traffic and long-term data processing demand fluctuations, demonstrating high flexibility and efficiency. The research results not only provide new ideas for the intelligent management and dynamic resource allocation of data centers, but also have important value in promoting the sustainable development of the data center industry.
