Safety Analysis of Rail Transit Redundant Structure in Cloud Computing Environment Based on Graph and Bayesian Theory

Kang, Yumeng; Bu, Bing; Gao, Bing

doi:10.1109/itsc55140.2022.9921932

Cited by 1 publication

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“…The reliability of the safety computing platform will suffer if the conventional overlaid method is continued as it will require more equipment, spend more money, and use more energy [1] .This led to the development of cloud computing, a cuttingedge computing technique with enormous storage capacity and adaptable resources that may lower the number of devices, save money, and have strong computational capabilities. Application of cloud computing to safety computer platforms is a technical requirement [2] .…”

Section: Introductionmentioning

confidence: 99%

Research on cloud-based safe computing platform resource prediction

Hao,

2024

Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023)

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In order to ensure the safe operation of cloud-based safe computing platform in train control system, it is crucial to accurately predict cloud platform resources. Due to the different number of trains at different times and sections, the signal software load in cloud based security computing platforms also varies. Resource data often accompanies high randomness and non-stationary characteristics, increasing the difficulty of prediction. A hybrid prediction model based on Sparse Search Algorithm (SSA), Variational Mode Decomposition (VMD), SE module, and Time Convolutional Network (TCN) is proposed to address the low prediction accuracy of traditional prediction methods and the lack of research on cloud platform in train control system. First, the original data is decomposed using the VMD optimized by SSA, and then each component is predicted using the SE-TCN model. Finally, integrate the prediction results of each component to obtain the final cloud resource usage prediction result. The experimental results show that, compared with other models, the Mean absolute error (MAE) of the proposed model on the memory data are reduced by 27%, and the CPU data are reduced by 24%, with high prediction accuracy.

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Section: Introductionmentioning

confidence: 99%

Research on cloud-based safe computing platform resource prediction

Hao,

2024

Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023)

View full text Add to dashboard Cite

show abstract

Safety Analysis of Rail Transit Redundant Structure in Cloud Computing Environment Based on Graph and Bayesian Theory

Cited by 1 publication

References 0 publications

Research on cloud-based safe computing platform resource prediction

Research on cloud-based safe computing platform resource prediction

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