The growing complexity of cloud computing infrastructures has intensified the need for intelligent, cost-aware, and performance-optimized task scheduling mechanisms capable of operating within heterogeneous and dynamically scalable environments. Classical CPU scheduling algorithms, including First-Come-First-Served and Shortest Job First, while foundational, demonstrate limited adaptability under multi-tenant cloud conditions characterized by elasticity, virtualization overhead, and variable pricing models. Simulation platforms such as CloudSim have provided researchers with structured environments for modeling and evaluating scheduling strategies; however, the integration of advanced nature-inspired metaheuristic algorithms into CloudSim-based CPU scheduling remains theoretically fragmented. This study proposes a comprehensive hybrid nature-inspired metaheuristic framework for cost-aware CPU task scheduling and adaptive resource provisioning within CloudSim-modeled heterogeneous cloud infrastructures. Drawing upon research on optimized Shortest Job First algorithms, cost-based scheduling models, heuristic task allocation strategies, and recent advancements in swarm-based optimization including Squirrel Search, Horse Herd Optimization, Tunicate Swarm Algorithm, and hybrid workflow metaheuristics, the proposed framework synthesizes deterministic scheduling principles with evolutionary intelligence. The methodology elaborates a multi-layered scheduling architecture that integrates CPU queue optimization, cost modeling, workflow dependency awareness, and adaptive global search strategies. Descriptive simulation-based evaluation demonstrates improvements in makespan stability, CPU utilization efficiency, cost minimization, and workload fairness compared to conventional scheduling baselines. The discussion critically examines convergence dynamics, scalability implications, heterogeneity sensitivity, and simulation-driven validation constraints. The findings contribute a unified theoretical and practical perspective on integrating next-generation bio-inspired metaheuristics into CPU scheduling paradigms for cloud computing environments modeled through CloudSim, thereby advancing intelligent resource orchestration for large-scale distributed systems.

Cloud Computing, CPU Scheduling, CloudSim, Metaheuristic Optimization

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