Novel load prediction in microservice architecture using attention mechanism-based deep LSTM networks

Abstract

Load balancing in microservice architecture is essential for optimizing resource utilization and maintaining high availability. Traditional load balancing algorithms like First-Come-First-Serve (FCFS) and Round Robin often lead to inefficiencies due to their inability to account for server capabilities and varying request sizes. Machine Learning (ML) offers a promising solution by predicting future load patterns and distributing requests more effectively. In this study, we propose an innovative, novel attention mechanism-based Long Short-Term Memory (LSTM) network for web server load prediction. Our methodology involves detailed data preprocessing, sequence creation, and scaling to prepare the NASA HTTP dataset for model training. The attention mechanism enhances the LSTM network’s ability to focus on relevant input sequences, significantly improving predictive accuracy. Compared to traditional algorithms such as linear regression, polynomial regression, L2 regularization, decision tree regression, XGBoost, and ARIMA, our model achieves the lowest Mean Squared Error (MSE) of 187,293.59 and Root Mean Squared Error (RMSE) of 432.77, with a strong R-squared value of 0.8532. This superior performance highlights the model’s effectiveness in capturing both short-term and long-term dependencies in the data. This novel predictive model can be used to integrate into dynamic and efficient load balancing frameworks. Accurate future load predictions from AMDLN in the microservices environment optimize resource utilization and save infrastructure costs by providing accurate future load predictions for scaling up and scaling down of microservices.