使用先进人工智能/机器学习模型的时间序列网络利用率关键绩效指标预测
Time Series Network Utilization KPI Forecasting Using Advanced AI/ML Models
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中文总结 AI 辅助
研究针对数据密集型应用等导致的网络性能问题，通过评估季节性分解等多种模型，利用通用接口数据集在MAPE等指标上进行基准测试，给出模型准确性与计算效率权衡的见解，助相关人员选最佳预测模型。
中文摘要 AI 辅助
数据密集型应用、云基础设施和物联网生态系统的迅速扩散，使得主动资源配置对于维持最佳网络性能至关重要。然而，网络管理员面临容量限制的持续挑战，传统的被动方法无法准确预测流量波动。这种无法预见需求的情况会导致成本高昂的过度配置、意外停机和服务质量下降，直接影响运营预算和业务连续性。为实现高效的容量规划，准确预测带宽利用率至关重要。本研究通过评估多种模型应对这一挑战，包括季节性分解、Prophet、随机森林、XGBoost、支持向量回归以及双向和卷积LSTM等先进深度学习架构，使用在平均绝对百分比误差（MAPE）、归一化均方根误差（NRMSE）和决定系数（R平方）指标上进行基准测试的通用接口数据集。最终，本研究提供了关于模型准确性和计算效率之间权衡的可操作见解，使工程师、运营商和企业主能够为其特定的基础设施需求选择最佳预测模型。
英文摘要
The rapid proliferation of data-intensive applications, cloud infrastructure, and IoT ecosystems has made proactive resource provisioning critical for maintaining optimal network performance. However, network administrators face a constant battle against capacity constraints, where traditional reactive approaches fail to accurately anticipate traffic fluctuations. This inability to foresee demand leads to costly over-provisioning, unexpected downtime, and degraded quality of service directly impacting operational budgets and business continuity. To achieve efficient capacity planning, accurate forecasting of bandwidth utilization is essential. This study addresses the challenge by evaluating a diverse spectrum of models including seasonal decomposition, Prophet, Random Forest, XGBoost, Support Vector Regression, and advanced deep learning architectures like bidirectional and Convolutional LSTMs - using a common interface dataset benchmarked across MAPE, NRMSE, and R-square metrics. Ultimately, this research delivers actionable insights into the trade-offs between model accuracy and computational efficiency, empowering engineers, operators, and business owners to select the optimal forecasting model for their specific infrastructure needs.
