Deep learning-based method for predicting high-dimensional and highly-variable cloud workload

Abstract

The present disclosure relates to a deep learning-based method for predicting a high-dimensional and highly-variable cloud workload, including the following steps: Step S1: obtaining historical workload data of a cloud data center, and carrying out preprocessing; Step S2: on the basis of a raw data set, predicting a future workload of a central processing unit by using a deep learning based prediction algorithm for cloud workloads (L-PAW) integrating a top-sparse auto-encoder (TSA) and a gated recurrent unit (GRU), and transmitting a predicted result to a cloud service provider (CSP); and Step S3: determining, by the CSP, a resource allocation strategy according to the predicted result, such that the cloud data center achieves load balancing. The present disclosure realizes adaptive and effective workload prediction, thereby effectively improving the efficient resource allocation efficiency in cloud computing.

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