Approach to hybrid load management in Fat-Tree web clusters

Abstract

The paper presents an approach to hybrid load management in a web cluster that is capable of providing adaptive request balancing based on load prediction and resilience to random web server failures. The proposed architecture is built upon the Fat-Tree topology, which ensures high scalability, structural redundancy, and efficient routing within the cluster network. The developed system performs load forecasting using moving average methods and Erlang-based queueing models, enabling the estimation of overload probabilities and proactive redistribution of computational resources. Four representative simulation scenarios were analyzed: baseline load, peak load, dynamic traffic variations, and random server failures. The obtained results demonstrate enhanced system reliability, reduced average response time, and more balanced utilization of cluster resources. In the context of rapidly growing web services and user traffic volumes, the issue of maintaining high reliability and efficiency of clustered infrastructures becomes increasingly significant. Even with robust topologies such as Fat-Tree, irregular traffic patterns and sudden surges in client requests can cause local overloads and performance degradation. Random node failures further complicate cluster management, necessitating the use of adaptive and predictive control mechanisms. The proposed model integrates Fat-Tree network simulation with statistical forecasting algorithms, forming the basis for proactive load management. This integration allows for minimizing service degradation risks, dynamically responding to workload changes, and maintaining stable operation of web infrastructures under partial node failures. The architecture shows strong potential for real-time implementation in large-scale distributed web systems. It can be further enhanced by incorporating machine learning or wavelet-based forecasting methods to improve the accuracy of load estimation and system adaptability.