aiqbench analysis
This research compares traditional statistical methods against deep learning architectures for predicting network bandwidth utilization, offering engineers practical guidance on balancing model accuracy with computational efficiency. By benchmarking various algorithms across standard metrics like MAPE and NRMSE, the study provides actionable insights for optimizing resource provisioning in data-intensive IoT and cloud environments without relying on reactive capacity management.
Time Series Network Utilization KPI Forecasting Using Advanced AI/ML Models
arXiv:2607.19974v1 Announce Type: cross Abstract: 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.
Abstract: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.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2607.19974 [cs.LG]
(or arXiv:2607.19974v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.19974 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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