Load forecasting model based on deep learning

In container cloud platforms, elasticity scaling is a key characteristic. Recently, proactive elasticity scaling strategies have gained prominence. Proactive scaling involves predicting future resource needs ahead of time, allowing for timely preparation such as resource preallocation or instance adjustments before load changes occur, thus mitigating elasticity lag. This approach not only facilitates prompt response to load variations but also significantly boosts resource utilization. However, implementing effective proactive elasticity scaling is no easy feat, with the core challenge being accurate forecasting of future loads. To address this challenge, we devised a forecasting model integrating dilated convolutions and Long Short-Term Memory (LSTM) networks, designed to capture both short-term volatility and long-term dependencies in load patterns. The model employs a dilated convolution residual network to construct a feature extraction component, simultaneously learning short-term volatility, sustained change, and periodicity in load, followed by LSTM to deepen the understanding of long-term temporal dependencies. Experimental results on the Alibaba Cluster showed that our model outperformed comparative models in terms of performance.