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A Workload Characterization Methodology Using Supervised and Unsupervised Deep Learning
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DOI：10.1109/ACCESS.2024.3509857.png)
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Abstract
En 中文
A recuring decision problem in data centers optimally matches the computational requirements of an arbitrary workload with the capabilities of available servers. As the first step in this decision-making process, it is essential to obtain a set of meaningful and accurate workload classifiers. However, traditional approaches, such as principal component analysis (PCA), cannot capture time-varying workload characteristics. To fill this gap, this study proposes a deep-learning workload analysis tool (DLWAT) to capture complex workload dynamics. The DLWAT tool uses a hybrid deep learning model that includes a convolutional neural network (CNN) and recurrent neural networks (RNN) to study workloads from a supervised learning perspective. The DLWAT tool uses a hybrid deep learning model that includes an autoencoder (AE) and k-means clustering for unsupervised learning studies. Experiments showed that the DLWAT tool could precisely classify workloads for both labelled datasets (supervised use cases) and unlabeled datasets (unsupervised use cases). In the comparison studies, the DLWAT tool yielded good results.
Keywords:
Benchmark testing
Accuracy
Deep learning
Convolutional neural networks
Brain modeling
Recurrent neural networks
Principal component analysis
Data models
Bidirectional control
Analytical models
Data mining
deep learning
machine learning
supervised learning
unsupervised learning
workload characterization