GMFO: an autonomic scheduling and resource provisioning framework using Grouped Moth-Flame Optimization for cloud platforms

Efficient task scheduling and resource provisioning remain major challenges in cloud platforms due to workload heterogeneity, resource variability, and conflicting Quality of Service (QoS) objectives. This paper proposes an autonomic scheduling framework based on Grouped Moth-Flame Optimization (GMFO) to improve cloud task-to-resource mapping under dynamic workload conditions. The main methodological contribution is the adaptation of Moth-Flame Optimization through grouped population search, local subgroup leadership, inter-group knowledge sharing, and fission-fusion based diversity control. These mechanisms are designed to strengthen the exploration-exploitation balance and reduce premature convergence in large-scale scheduling spaces. The proposed framework formulates scheduling as a multi-objective optimization problem that considers makespan, execution cost, energy consumption, task acceptance, and load balancing. CloudSim-based experiments compare GMFO with established metaheuristic schedulers, including SMO, EGWO, PSO, BAT, and ABC. The results show that GMFO provides more stable and effective scheduling performance across the evaluated QoS metrics. Overall, the study demonstrates that grouping-driven MFO can provide a scalable and adaptive basis for autonomic cloud scheduling.