Enhancing Microservices Performance with AI-Based Load Balancing: A Deep Learning Perspective

Microservices architectures have become a cornerstone of modern software engineering, enabling scalability and flexibility in distributed systems. However, efficient load balancing under variable traffic remains a challenge. This study proposes a deep learning-based approach to enhance load balancing in microservices, leveraging Long Short-Term Memory (LSTM) networks to predict traffic patterns and optimize resource distribution. The proposed model is evaluated against traditional load balancing algorithms such as round-robin and least connections, using a unique dataset of synthetic and real-world traffic traces from Kubernetes clusters. Performance metrics, including latency, throughput, and resource utilization, demonstrate the superiority of the AI-driven approach, achieving up to 25% lower latency and 30% higher throughput compared to baselines. Bar graphs, line graphs, and tables illustrate comparative analysis, highlighting the model’s effectiveness in dynamic environments.