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Cloud computing load prediction method based on CNN-BiLSTM model under low-carbon background.

HaoFang Zhang1, Jie Li2, HaoRan Yang3

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This summary is machine-generated.

This study introduces a CNN-BiLSTM model to predict cloud computing load and carbon emissions, crucial for the "double carbon" goal. The model significantly reduces prediction errors, aiding low-carbon emission reduction efforts.

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Area of Science:

  • Computer Science
  • Environmental Science
  • Artificial Intelligence

Background:

  • Cloud data centers face challenges in matching resource supply with load requests, leading to significant carbon emissions.
  • The global imperative to achieve
  • double carbon
  • goals necessitates innovative solutions for emission reduction across industries.
  • Inefficient resource allocation in cloud computing contributes to energy waste and increased carbon footprint.

Purpose of the Study:

  • To develop and validate a novel method for predicting carbon emissions from cloud computing environments.
  • To establish a dynamic server carbon emission prediction model that correlates emissions with CPU utilization.
  • To contribute to achieving low-carbon emission reduction targets within the cloud computing sector.

Main Methods:

  • Utilized a combined Convolutional Neural Network (CNN) and Bidirectional Long-Term and Short-Term Memory (BiLSTM) neural network model for cloud computing load prediction.
  • Integrated predicted load with power calculations to estimate real-time carbon emissions.
  • Developed a dynamic server carbon emission prediction model based on CPU utilization.
  • Employed Google cluster data for model training and validation.

Main Results:

  • The CNN-BiLSTM model demonstrated superior performance in cloud computing load prediction compared to traditional models.
  • Achieved significant reductions in Mean Squared Error (MSE) by 52%, 50%, 34%, and 45% against BP, LSTM, BiLSTM, and CEEMDAN-ConvLSTM models, respectively.
  • The dynamic server carbon emission model effectively linked server emissions to CPU utilization.

Conclusions:

  • The CNN-BiLSTM model is highly effective for predicting cloud computing load and subsequent carbon emissions.
  • The proposed dynamic prediction model offers a viable strategy for reducing carbon emissions in cloud data centers.
  • This research provides a valuable tool for optimizing cloud resource management towards environmental sustainability.