Enhancing Load Balancing Efficiency in Distributed Systems through Linear Programming Techniques in WSCLB

Optimizing network performance and resource allocation fairness in distributed systems requires load balancing efficiency. The main goal is to design and deploy Linear Programming (LP) methods in Weighted Server Cluster Load Balancer (WSCLB) to accomplish this goal. These methods reallocate jobs between nodes to reduce load distribution discrepancies, avoiding bottlenecks and boosting system performance. The purpose is to balance load, so each node runs at optimum capacity, lowering latency and improving distributed system resilience. The solution uses LP to optimize load distribution in WSCLB, creating a more robust and efficient network that can handle variable demand without performance deterioration. The study emphasizes mathematical optimization's role in current distributed system load balancing. The first instance of Load_Distribution_Pre-Optimization showed 5 load levels for 5 nodes. In load level 1, the top and lower values are 93 and 15, in load level 2, 87 and 21, in load level 3, 88 and 24, in load level 4, 75 and 10, and in load level 5, 89 and 44. In Load_Distribution_Post-Optimization 2, 5 load levels for 5 nodes were observed. In load level 1, the top and lower values are 93 and 29, in load level 2, 96 and 10, in load level 3, 72 and 49, in load level 4, 94 and 42, and in load level 5, 91 and 14.