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Burst Load Frequency Prediction Based on Google Cloud Platform Server
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DOI：10.1109/TCC.2024.3449884.png)
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The widespread use of cloud computing platforms has increased server load pressure. Especially the frequent occurrence of burst load problems caused resource waste, data damage and loss, and security loopholes, which have posed a severe threat to the service capabilities and stability of the cloud platform. To reduce or avoid the harm caused by burst load problems, this article conducts in-depth research on the frequency of burst loads. Based on Google cluster tracking data, this paper proposes a new burst load frequency calculation model called the Two-step Judgment and a burst load frequency prediction model called the Combined-LSTM. The Two-step Judgment model uses data attributes for rough judgment and then uses the random forest algorithm for precise judgment to ensure accurate calculation of the frequency of burst loads. The Combined-LSTM model is a multi-input single-output prediction model constructed using a multi-model ensemble method. This model combines the advantages of the 1-Dimensional Convolutional Neural Network(1D-CNN), Gated Recurrent Unit(GRU), and Long Short-Term Memory(LSTM) and uses parallel computing methods to achieve accurate prediction of burst load frequency. According to the model evaluation, the Two-step Judgment model and the Combined-LSTM model showed significant advantages over other prediction models in accuracy, generalization ability, and time complexity.
Keyword:
Predictive models
Load modeling
Cloud computing
Data models
Computational modeling
Feature extraction
Accuracy
Burst load frequency
cloud computing
google cluster trace data
long short-term memory
multi-model ensemble
random forest
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被引数:
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