In the era of big data, managing dynamic data flows efficiently is crucial as traditional storage models struggle with real-time regulation and risk overflow. This paper introduces Data Dams, a novel framework designed to optimize data inflow, storage, and outflow by dynamically adjusting flow rates to prevent congestion while maximizing resource utilization. Inspired by physical dam mechanisms, the framework employs intelligent sluice controls and predictive analytics to regulate data flow based on system conditions such as bandwidth availability, processing capacity, and security constraints. Simulation results demonstrate that the Data Dam significantly reduces average storage levels (371.68 vs. 426.27 units) and increases total outflow (7999.99 vs. 7748.76 units) compared to static baseline models. By ensuring stable and adaptive outflow rates under fluctuating data loads, this approach enhances system efficiency, mitigates overflow risks, and outperforms existing static flow control strategies. The proposed framework presents a scalable solution for dynamic data management in large-scale distributed systems, paving the way for more resilient and efficient real-time processing architectures.

翻译：在大数据时代，高效管理动态数据流至关重要，因为传统存储模型难以应对实时调控和溢出风险。本文提出数据坝（Data Dams）这一新型框架，通过动态调整流速来防止拥塞并最大化资源利用率，从而优化数据流入、存储和流出过程。该框架受物理水坝机制启发，采用智能闸门控制和预测分析技术，依据带宽可用性、处理能力及安全约束等系统状态来调控数据流。仿真结果表明，与静态基线模型相比，数据坝能显著降低平均存储水平（371.68 vs. 426.27单位）并提升总流出量（7999.99 vs. 7748.76单位）。通过在波动数据负载下确保稳定且自适应的流出速率，该方法提升了系统效率，降低了溢出风险，并优于现有的静态流量控制策略。所提出的框架为大规模分布式系统中的动态数据管理提供了可扩展解决方案，为构建更具韧性和高效性的实时处理架构奠定了基础。