Parallel Hybrid Metaheuristic and Branch-and-Bound Scheduler

Task scheduling in cloud computing is a critical optimization challenge due to resource heterogeneity and dynamic workloads. Metaheuristic algorithms, such as the Flower Pollination Algorithm (FPA), provide fast and near-optimal solutions but may lack precision in complex scenarios. After a comparative evaluation on the GoCJ dataset, FPA was selected for its favorable trade-off between solution quality and execution time. This work introduces HMBB-Sched, a hybrid algorithm that combines FPA with the exact Branch and Bound (B&B) method to enhance solution quality and convergence.To further improve scalability, we propose Parallel_HMBB-Sched, which processes task clusters in parallel using multithreading. All experiments were conducted using CloudSim and the GoCJ dataset, ensuring consistency and reliability of the evaluation. Results show that the hybrid method significantly reduces makespan and execution time compared to standalone FPA. The multithreaded version achieves considerable speedups, particularly under high workloads. This study highlights the effectiveness of combining hybridization and parallelism for efficient and scalable task scheduling in cloud environments.