Optimizing Cloud Computational Performance via Hybridizing Rock Hyrax and Grey Wolf Optimization Algorithms

Task scheduling in cloud computing presents major challenges due to dynamic workloads, resource heterogeneity, and the inherent limitations of traditional optimization methods in maintaining an effective balance between exploration and exploitation. Existing algorithms like Rock Hyrax Optimization (RHO) and Grey Wolf Optimization (GWO) have individual drawbacks: RHO struggles with slower convergence speeds, while GWO often risks premature convergence, resulting in inefficient scheduling outcomes. To overcome these limitations, this research focuses on developing a more adaptive and efficient scheduling model suited for cloud data centers, edge computing, and real-time applications. A novel hybrid RHO-GWO optimization model is proposed, which dynamically balances exploration and exploitation based on the nature of workload conditions. The model intelligently adapts, selecting the optimal strategy during task scheduling to enhance overall performance. Implementation and evaluation were conducted within simulated cloud environments, assessing key performance metrics such as makespan, resource utilization, load balancing, and energy efficiency. The results demonstrate that the Hybrid RHO-GWO algorithm outperforms RHO and GWO across key metrics. It achieves, the lowest makespan (65s–160s), the highest resource efficiency (0.91), the best load balancing (variance: 0.050–0.053 and average variance at 0.88), and the lowest energy consumption (950W), offering up to 20.83% energy savings, making it the most efficient and balanced solution among the three.