MambaNet0: Mamba-Based Sustainable Cloud Resource Prediction Framework Towards Net Zero Goals

Thananont Chevaphatrakul, Han Wang and Sukhpal Singh Gill ()
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Thananont Chevaphatrakul: School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
Han Wang: School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
Sukhpal Singh Gill: School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK

Future Internet, 2025, vol. 17, issue 10, 1-13

Abstract: With the ever-growing reliance on cloud computing, efficient resource allocation is crucial for maximising the effective use of provisioned resources from cloud service providers. Proactive resource management is therefore critical for minimising costs and striving for net zero emission goals. One of the most promising methods involves the use of Artificial Intelligence (AI) techniques to analyse and predict resource demand, such as cloud CPU utilisation. This paper presents MambaNet0, a Mamba-based cloud resource prediction framework. The model is implemented on Google’s Vertex AI workbench and uses the real-world Bitbrains Grid Workload Archive-T-12 dataset, which contains the resource usage metrics of 1750 virtual machines. The Mamba model’s performance is then evaluated against established baseline models, including Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Amazon Chronos, to demonstrate its potential for accurate prediction of CPU utilisation. The MambaNet0 model achieved a 29% improvement in Symmetric Mean Absolute Percentage Error (SMAPE) compared to the best-performing baseline Amazon Chronos. These findings reinforce the Mamba model’s ability to forecast accurate CPU utilisation, highlighting its potential for optimising cloud resource allocation in contribution to net zero goals.

Keywords: cloud computing; Artificial Intelligence; time-series forecasting; Mamba; net zero emissions (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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