Spectra Net: Hybrid Time and Frequency-Domain Modeling for Sustainable Cloud CPU Prediction
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Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh
Abstract
The proliferation of cloud computing has created an urgent need for sustainable resource
management strategies to mitigate the growing energy consumption and carbon footprint
of data centers. Accurate CPU workload forecasting is a cornerstone of this effort, en abling proactive resource allocation that minimizes energy waste from over-provisioning,
and prevents performance degradation from under-provisioning. This study introduces
SpectraNet, a lightweight hybrid model that advances sustainable cloud computing by
delivering highly accurate CPU usage predictions with minimal computational overhead.
By integrating time domain and frequency-domain analysis, SpectraNet effectively cap tures both transient and periodic workload patterns. Experimental results on the Azure VM
CPU Readings and Alibaba Cloud Workload datasets demonstrate that this dual-domain
approach significantly improves forecasting accuracy. SpectraNet demonstrates an com petitive balance of performance and efficiency, achieving a Mean Absolute Error (MAE)
of 0.0549 on long-range forecasts. Critically, the model achieves this accuracy while being
up to 19 times smaller (with only 89,805 parameters) and 5 times faster at inference than
larger, more complex architectures. The model’s efficiency and small footprint make it a
practical and scalable solution for real world, resource-constrained cloud environments. By
enabling more precise and energy-efficient resource management, SpectraNet contributes
a valuable tool for building more sustainable and cost-effective cloud infrastructures.
Description
Supervised by
Dr. Md. Azam Hossain,
Associate Professor,
Mr. Aashnan Rahman,
Junior Lecturer,
Department of Computer Science and Engineering (CSE)
Islamic University of Technology (IUT)
Board Bazar, Gazipur, Bangladesh
This thesis is submitted in partial fulfillment of the requirement for the degree of Bachelor of Science in Computer Science and Engineering, 2025
Citation
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