Enhancing Cloud Resource Utilization Through Synthesizing

Effective scheduling and resource management remain an enduring challenge in large-scale cloud computing contexts. Intermittent workloads, coupled with workloads that have dependencies, can affect system performance. This paper proposes a novel hybrid model, VmSS–TGDA (Virtual Machine Speed Selection with Task Grouping and Dependency Analysis), to improve cloud resource utilization. The approach centers on simultaneous execution time optimization, energy efficiency, and resource fairness. The proposed workflow units are produced by using GScore-based grouping and probabilistic task dependency prediction to generate workflows with high levels of correlation. The workflow units are then executed according to an Integer Linear Programming (ILP) formulation that holds the virtual machine speed, or the selected speed of the virtual machine, as the guiding metric. The theoretical structure and framework of the presented model is based on a set of analytical equations that include all aspects of delay, utilization, energy, and reliability and establish the relationships between them. The experimental results confirm that we can achieve substantial improvements in both Task Throughput Efficiency (TTE) and Resource Allocation Fairness Index (RAFI) when compared with legacy scheduling models, achieving up to 22% reductions in execution times and up to 35% reductions in resource utilizations. This research also confirms the balance between cost-effectivity and efficiently processing all workloads using a correlation heatmap and 3D multi-objective performance surface. Ultimately, Ultimately, the VmSS–TGDA paradigm provides theory-informed empirical and experimental evidence of improved cloud resource utilization in large-scale cloud systems.