Abstract
In modern data centers, managing the distribution of workloads efficiently is crucial
for ensuring optimal performance and meeting Service Level Agreements (SLAs). Load balancing
algorithms play a vital role in this process by distributing workloads across computing resources
to avoid overloading any single resource. However, the effectiveness of these algorithms can be
significantly enhanced through the integration of advanced optimization techniques. This paper
proposes an SLA-driven load balancing algorithm optimized using methods such as genetic
algorithms, particle swarm optimization, and simulated annealing. By focusing on both resource
utilization and SLA compliance, the proposed approach aims to reduce latency, improve
throughput, and maximize overall system efficiency.