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Copyright of Science Technology & Engineering is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Science Technology & Engineering is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Predictive analysis of cloud resources is very important for responding to resource requests and making timely decisions. For resource management issues such as over-allocation and insufficient supply of container cloud resources, low cloud resource prediction accuracy and data volatility issues, in order to make cloud resource predictions work load responding to the demand in advance and making a reasonable allocation, a cloud resource combination forecasting model was proposed that based on Holt-Winters and long short-term memory network (HW-LSTM), and the coefficient of variation of the forecast residuals was used to give weights. Prediction experiments on Amazon's CPU data set show that the combined model has better prediction performance and stability than Holt-Winters, LSTM and convolutional neural networks(CNN) models. The optimization ranges of root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), mean absolute scaled error ( MASE) and variance D( n) are 0. 065 ~ 1. 026, 0. 023 ~ 0. 269, 0. 001 ~ 0. 007, 0. 004 ~ 0. 039 and 0. 079 ~ 4. 125 respectively. [ABSTRACT FROM AUTHOR]

Copyright of Science Technology & Engineering is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Science Technology & Engineering is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)