Zhang HaoFang, Li Jie, Yang HaoRan
Ninghe Campus Management Center, Civil Aviation University of China, Tianjin, 300000, China.
College of Computer and Information Engineering, Tianjin Normal University, Tianjin, 300000, China.
Sci Rep. 2024 Aug 3;14(1):18004. doi: 10.1038/s41598-024-68339-1.
With the establishment of the "double carbon" goal, various industries are actively exploring ways to reduce carbon emissions. Cloud data centers, represented by cloud computing, often have the problem of mismatch between load requests and resource supply, resulting in excessive carbon emissions. Based on this, this paper proposes a complete method for cloud computing carbon emission prediction. Firstly, the convolutional neural network and bidirectional long-term and short-term memory neural network (CNN-BiLSTM) combined model are used to predict the cloud computing load. The real-time prediction power is obtained by real-time prediction load of cloud computing, and then the carbon emission prediction is obtained by power calculation. Develop a dynamic server carbon emission prediction model, so that the server carbon emission can change with the change of CPU utilization, so as to achieve the purpose of low carbon emission reduction. In this paper, Google cluster data is used to predict the load. The experimental results show that the CNN-BiLSTM combined model has good prediction effect. Compared with the multi-layer feed forward neural network model (BP), long short-term memory network model (LSTM ), bidirectional long short-term memory network model (BiLSTM), modal decomposition and convolution long time series neural network model (CEEMDAN-ConvLSTM), the MSE index decreased by 52 , 50 , 34 and 45 respectively.
随着“双碳”目标的确立，各行业都在积极探索降低碳排放的途径。以云计算为代表的云数据中心，常存在负载请求与资源供应不匹配的问题，导致碳排放过高。基于此，本文提出一种完整的云计算碳排放预测方法。首先，利用卷积神经网络与双向长短时记忆神经网络（CNN-BiLSTM）组合模型预测云计算负载。通过对云计算负载进行实时预测得到实时预测功率，再通过功率计算得到碳排放预测值。开发动态服务器碳排放预测模型，使服务器碳排放能随CPU利用率的变化而变化，从而达到低碳减排的目的。本文利用谷歌集群数据进行负载预测。实验结果表明，CNN-BiLSTM组合模型具有良好的预测效果。与多层前馈神经网络模型（BP）、长短时记忆网络模型（LSTM）、双向长短时记忆网络模型（BiLSTM）、模态分解与卷积长时序神经网络模型（CEEMDAN-ConvLSTM）相比，MSE指标分别下降了52%、50%、34%和45%。
