Cloud resource prediction model based on triple exponential smoothing method and temporal convolutional network
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 a...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2019-08-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019172/ |
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| _version_ | 1841539333441454080 |
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| author | Xiaolan XIE Zhengzheng ZHANG Jianwei WANG Xiaochun GHENG |
| author_facet | Xiaolan XIE Zhengzheng ZHANG Jianwei WANG Xiaochun GHENG |
| author_sort | Xiaolan XIE |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2988586f80fa4dc6b79da7c1bcddaa61 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2019-08-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-2988586f80fa4dc6b79da7c1bcddaa612025-01-14T07:17:33ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2019-08-014014315059729166Cloud resource prediction model based on triple exponential smoothing method and temporal convolutional networkXiaolan XIEZhengzheng ZHANGJianwei WANGXiaochun GHENGThe 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.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019172/resource predictionKubernetesexponential smoothing methodtemporal convolutional network |
| spellingShingle | Xiaolan XIE Zhengzheng ZHANG Jianwei WANG Xiaochun GHENG Cloud resource prediction model based on triple exponential smoothing method and temporal convolutional network Tongxin xuebao resource prediction Kubernetes exponential smoothing method temporal convolutional network |
| title | Cloud resource prediction model based on triple exponential smoothing method and temporal convolutional network |
| title_full | Cloud resource prediction model based on triple exponential smoothing method and temporal convolutional network |
| title_fullStr | Cloud resource prediction model based on triple exponential smoothing method and temporal convolutional network |
| title_full_unstemmed | Cloud resource prediction model based on triple exponential smoothing method and temporal convolutional network |
| title_short | Cloud resource prediction model based on triple exponential smoothing method and temporal convolutional network |
| title_sort | cloud resource prediction model based on triple exponential smoothing method and temporal convolutional network |
| topic | resource prediction Kubernetes exponential smoothing method temporal convolutional network |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019172/ |
| work_keys_str_mv | AT xiaolanxie cloudresourcepredictionmodelbasedontripleexponentialsmoothingmethodandtemporalconvolutionalnetwork AT zhengzhengzhang cloudresourcepredictionmodelbasedontripleexponentialsmoothingmethodandtemporalconvolutionalnetwork AT jianweiwang cloudresourcepredictionmodelbasedontripleexponentialsmoothingmethodandtemporalconvolutionalnetwork AT xiaochungheng cloudresourcepredictionmodelbasedontripleexponentialsmoothingmethodandtemporalconvolutionalnetwork |