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Leveraging Sparse Auto-Encoding and Dynamic Learning Rate for Efficient Cloud Workloads Prediction
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DOI：10.1109/ACCESS.2023.3289884.png)
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Abstract
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Cloud computing provides simple on-demand access to a centralized shared pool of computing resources. Performance and efficient utilization of cloud computing resources requires accurate prediction of cloud workload. This is a challenging problem due to cloud workloads' dynamic nature, making it difficult to predict. Here we leverage deep learning which can with proper training provide accurate bases for the prediction of data center workload. Deep Learning (DL) models, however, are challenging to train. One challenge is the vast number of hyperparameters needed to define and tune. The performance of a neural network model can be significantly improved by optimizing these hyperparameters. We recognize two of the essential issues to predict data center workloads using deep learning efficiently. First is the high dimensionality which requires removing superfluous information via some form of dimension reduction. Secondly, is the learning rate. Small learning rates can make the time for training very excessive while long learning rates can miss optimal solutions. Our approach is therefore dual-pronged. First, we use Sparse Auto-Encoder (SAE) to retrieve the essential workloads representations from the original high-dimensional historical cloud workloads data. Secondly, we use Gated Recurrent Unit with a Step-Wise Scheduler for the Learning Decay (GRU-SWSLD). The proposed system is demonstrated with data from Google cluster workload traces to predict Central Processing Unit (CPU) usage using the data center's workload traces at several consecutive time steps. Our experimental results reveal that our proposed methodology provides a better tradeoff between accuracy and training time when compared with other models.
Keywords:
Cloud computing
deep learning
sparse auto-encoder
gated recurrent unit
learning rate
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29.4W
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