In cloud computing, the elastic scaling load prediction algorithm is an important technology used to ensure the reliability and stability of the cloud computing platform. The main purpose of this algorithm is to predict the size of the future load, so that measures can be taken in advance, such as increasing the size of the resource pool or adjusting the configuration of the server to deal with the possible load increase.
In traditional elastic scaling load forecasting algorithms, some simple methods are usually used to predict the size of future loads. For example, real-time load data can be smoothed using an exponential smoothing algorithm or a moving average algorithm, and the smoothed data can be used to predict the size of future loads.
However, there are some problems with these traditional algorithms. First, they usually only provide coarse-grained forecasts and cannot precisely predict the size of future loads. Second, they typically cannot accommodate complex load patterns, such as periodic loads or burst loads.
To solve these problems, this paper proposes a new elastic scaling load prediction algorithm. The algorithm uses deep learning techniques to predict the size of future loads by building a multi-layer perceptron (MLP) model. The model uses real-time load data as input and trains the model with training data to improve prediction accuracy.
Compared with traditional elastic scaling load prediction algorithms, the algorithm proposed in this paper has the following advantages. First, it provides more accurate forecasts and is able to accommodate complex load patterns. Second, the algorithm has better adaptability and scalability, and can automatically adjust the forecasting model as the load changes, thereby improving forecasting accuracy and reliability.
In the experimental section, we evaluate the proposed algorithm using a real cloud computing platform dataset. The experimental results show that the algorithm proposed in this paper has higher prediction accuracy and better adaptability than the traditional elastic scaling load prediction algorithm.
Overall, the elastic scaling load prediction algorithm proposed in this paper provides a new solution for the management of cloud computing platforms. It provides more accurate forecasts and adapts to complex load patterns, improving the reliability and stability of cloud computing platforms.
This article is published by mdnice multi-platform