用智能水闸调控数据流，防拥堵提效率。
Data Dams: A Novel Framework for Regulating and Managing Data Flow in Large-Scale Systems
- 借鉴物理水坝原理，动态调节数据流入速度。
- 存储量降12.8%，出流量增3.2%，显著优于静态模型。
- 适合高并发实时系统，提升稳定性与资源利用率。
在大数据时代，高效管理动态数据流至关重要，传统存储模型难以应对实时调控和溢出风险。本文提出Data Dams框架，通过智能水闸控制与预测分析，动态调节数据流入、存储与流出速率，以适应带宽、处理能力及安全约束等系统条件。仿真结果表明，相比静态基线模型，该框架将平均存储水平从426.27单位降至371.68单位（降幅12.8%），总出流量从7748.76单位提升至7999.99单位（增幅3.2%）。该方法在波动数据负载下保持稳定自适应出流，有效降低溢出风险，提升系统效率，为大规模分布式系统的动态数据管理提供可扩展方案。
原文摘要 · Abstract (English)
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.
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