随着“双碳”目标的确立,各行业都在积极探索减少碳排放的方法。云数据中心,以云计算为代表,通常存在加载请求和资源供应之间不匹配的问题,导致过量的碳排放。基于此,本文提出了一种完整的云计算碳排放预测方法。首先,卷积神经网络和双向长短期记忆神经网络(CNN-BiLSTM)组合模型用于云计算负荷预测。实时预测能力通过云计算的实时预测负荷,然后通过功率计算得到碳排放预测。建立动态服务器碳排放预测模型,使服务器碳排放随CPU利用率的变化而变化,从而达到低碳减排的目的。在本文中,Google集群数据用于预测负载。实验结果表明,CNN-BiLSTM组合模型具有较好的预测效果。与多层前馈神经网络模型(BP)相比,长短期记忆网络模型(LSTM),双向长短期记忆网络模型(BiLSTM),模态分解和卷积长时间序列神经网络模型(CEEMDAN-ConvLSTM),MSE指数下降了52%,50%,分别为34%和45%。
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.