A novel hybrid multi-resource load balancing approach using ant colony optimization with Tabu search for cloud computing

Cloud computing has become an increasingly important way to process large and complex jobs and services. A powerful scheduler for cloud users' workloads must serve millions of users satisfied with cost and time. This paper designs a novel hybrid approach by integrating the ant colony optimization (ACO) with the Tabu search (TS) approach for multi-resource load balancing. The performance metrics, such as makespan, average throughput, and total cost are calculated and evaluated with the help of these metrics. The proposed ACOTS approach performs better than the existing four optimization approaches: GA, PSO, ACO, and TS. The proposed ACOTS approach performed 30% better than GA, PSO, ACO, and TS algorithms in data delivery. The proposed ACOTS shows the fast file delivery and processing.