Cloud resource prediction model based on triple exponential smoothing method and temporal convolutional network
Article
Xie, X., Zhang, Z., Wang, J. and Cheng, X. 2019. Cloud resource prediction model based on triple exponential smoothing method and temporal convolutional network. Journal on Communications. 40 (8), pp. 143-150. https://doi.org/10.11959/j.issn.1000-436x.2019172
| Type | Article |
|---|---|
| Title | Cloud resource prediction model based on triple exponential smoothing method and temporal convolutional network |
| Authors | Xie, X., Zhang, Z., Wang, J. and Cheng, X. |
| 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. |
| Keywords | resource prediction, Kubernetes, exponential smoothing method, temporal convolutional network |
| Publisher | China Association for Science and Technology (CAST) |
| Journal | Journal on Communications |
| ISSN | 1000-436X |
| Publication dates | |
| 31 Aug 2019 | |
| Online | 01 Jan 2020 |
| Publication process dates | |
| Deposited | 12 Mar 2020 |
| Accepted | 01 Nov 2019 |
| Output status | Published |
| Copyright Statement | Copyright © 2018 Journal on Communications, All Rights Reserved. |
| Digital Object Identifier (DOI) | https://doi.org/10.11959/j.issn.1000-436x.2019172 |
| Language | English |
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