Leveraging Ant Colony Optimization for Adaptive Load Balancing in Cloud Computing Environments

Traffic flow optimization using ant colony optimization for adaptive load balancing in clouds These technical abstract focuses on Ant Colony Optimization (ACO) in achieving adaptive load balancing in cloud computing. Optimizing the load distribution policies is critical to maximize resource utilization and reduce latency and response time, considering the dynamic nature of the cloud workloads. Ant Colony Optimization (ACO) is a swarm intelligence-based algorithm that draws inspiration from the foraging actions of ants and presents a decentralized and self-organized framework, making it fascinatingly suitable for such problems. We allocate the ACO-based adaptive load balancing algorithm in this study, where it instantly adapts to the input conditions where the pheromone trail effects are simulated, and we apply the heuristic information of the input that considers network and resource metrics when allocating loads. The algorithm continuously adaptively updates routing paths according to real-time load states. The experimental results show that the proposed method can balance the workload better and reduce the task completion time compared to conventional load-balancing methods. The technique still displays robustness in a changing workload and scalability in a big cloud environment. Additionally, it has a light footprint, making it feasible to employ in resource-constrained environments.