Implementing ML Models in Load Balancing to Improve Application Performance

Abstract

In modern distributed systems, load balancing plays a critical role in ensuring optimal performance and user experience. However, traditional static load balancing mechanisms often fail to adapt to dynamic traffic patterns, leading to performance degradation, increased latency, and inefficient resource utilization. This paper presents a novel approach that leverages machine learning (ML) models to enhance load balancing by predicting traffic fluctuations and intelligently distributing workloads in real time.

By training ML models on historical traffic data and application performance metrics, we enable the system to make proactive decisions about resource allocation. This approach improves the ability to handle traffic surges during peak periods, minimizes latency, and optimizes infrastructure usage. The research outlines the implementation of various ML techniques, such as reinforcement learning and neural networks, into a microservices-based architecture, demonstrating how these models enhance both load balancing and auto-scaling capabilities.

Empirical results from the study reveal that ML-driven load balancing reduces latency by up to 40%, improves resource efficiency, and lowers infrastructure costs by 30%, compared to traditional methods. The paper concludes by discussing the technical challenges, future possibilities of using more advanced ML algorithms, and the broader implications for cloud-native application performance.