Dynamic Weight Assignment with Least Connection Approach for Enhanced Load Balancing in Distributed Systems

Abstract

Load balancing is a critical aspect of managing server resources efficiently and ensuring optimal performance in distributed systems. The Weighted Round Robin (WRR) algorithm is commonly used to allocate incoming requests among servers based on their assigned weights. However, static weights may not reflect the changing demands of servers, leading to imbalanced workloads. To address this issue, this study proposes a dynamic mechanism for assigning weights to servers in the WRR algorithm based on the data rate and incorporates the Least Connection approach for the best result. The dynamic mechanism takes into account the real-time data rate of each server, representing its current load. Servers with higher data rates are assigned higher weights to attract a larger share of incoming requests, while those with lower data rates receive lower weights to manage their loads effectively. This dynamic weight assignment allows the algorithm to adapt to varying workloads and achieve better load balancing across servers. To further refine the distribution of requests, the Least Connection approach is employed to handle tie-breaking situations and for more fairness in distributing the loads. The proposed algorithm is a hybrid of data rate and the Least Connection, it is evaluated through simulations and real-world experiments. The results demonstrate its superiority in achieving improved load balance compared to traditional static-weight WRR algorithms. By dynamically adjusting weights based on data rate and employing the Least Connection approach, the algorithm optimizes server resource usage, minimizes response times, and enhances overall system performance in distributed environments.