Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (12): 147-156.doi: 10.11896/jsjkx.231000098

• High Performance Computing • Previous Articles     Next Articles

Load Prediction Method of Cloud Resource Based on v-Informer

YOU Wenlong, DENG Li, LI Ruilong, XIE Yuxin, REN Zhengwei   

  1. College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China
    Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan 430065, China
  • Received:2023-10-16 Revised:2024-03-11 Online:2024-12-15 Published:2024-12-10
  • About author:YOU Wenlong,born in 1999,postgra-duate.His main research interests include cloud computing and artificial intelligence.
    DENG Li,born in 1972,Ph.D,associate professor,is a member of CCF(No.57882M).Her main research interests include cloud computing and distributed computing.
  • Supported by:
    National Natural Science Foundation of China(61902285).

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Abstract: Cloud computing technology is widely used at present.With the increase in the number of users,the allocation and management of cloud computing resources is becoming more and more important,and accurate load prediction is an important basis for allocation and management.Based on the Informer model,this paper proposes a long-term CPU load prediction method for high dynamic cloud platform tasks,called v-Informer.v-Informer decomposes the variation trend in the load sequence through va-riational mode decomposition,and introduces a multi-head self-attention mechanism to capture the long-term dependence and local nonlinear relationship.At the same time,the gradient concentration technique is used to improve the optimizer and reduce the computational cost.Experiments are carried out on the data of Microsoft and Google cloud platforms.The results show that,compared with the existing CPU load prediction models LSTM,Transformer,TCN and CEEMDAN-Informer,the prediction error of v-Informer is reduced by 34%,19%,15% and 6.5% respectively on the Google dataset.The prediction error on the Microsoft dataset is reduced by 32%,16%,12% and 7% respectively,with better prediction accuracy.

Key words: Cloud platform, CPU load, Multi-step forecasting, Modal decomposition, Informer, Gradient convergence

CLC Number: 

  • TP391
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