Cloud resource prediction model based on triple exponential smoothing method and temporal convolutional network |
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Authors: | Xiaolan XIE Zhengzheng ZHANG Jianwei WANG Xiaochun GHENG |
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Institution: | 1. Guangxi Key Laboratory of Embedded Technology and Intelligent System,Guilin University of Technology,Guilin 541004,China;2. College of Information Science and Engineering,Guilin University of Technology,Guilin 541004,China;3. Department of Computer Science,Middlesex University,London NW4 4BT,UK |
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Abstract: | The container cloud represented by Docker and Kubernetes has the advantages of less additional resource overhead and shorter start-up and destruction time.However there are still resource management issues such as over-supply and under-supply.In order to allow the Kubernetes cluster to respond “in advance” to the resource usage of the applications deployed on it,and then to schedule and allocate resources in a timely,accurate and dynamic manner based on the predicted value,a cloud resource prediction model based on triple exponential smoothing method and temporal convolutional network was proposed,based on historical data to predict future demand for resources.To find the optimal combination of parameters,the parameters were optimized using TPOT thought.Experiments on the CPU and memory of the Google dataset show that the model has better prediction performance than other models. |
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Keywords: | resource prediction Kubernetes exponential smoothing method temporal convolutional network |
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