Hybrid Metaheuristic-Driven Intelligent Task Scheduling and Resource Allocation Framework for Dynamic Cloud Computing Environments

Abstract

The rapid evolution of cloud computing has intensified the demand for efficient task scheduling and resource allocation mechanisms capable of handling dynamic, heterogeneous, and large-scale workloads. Traditional scheduling algorithms often struggle to balance performance metrics such as makespan, resource utilization, energy efficiency, and quality of service under fluctuating demand conditions. Inspired by advancements in bio-inspired and evolutionary optimization techniques, this study proposes a comprehensive hybrid metaheuristic-driven framework for intelligent task scheduling and adaptive resource distribution in dynamic cloud computing environments. Drawing upon established research in genetic algorithms, particle swarm optimization, differential evolution, ant colony optimization, artificial bee colony algorithms, and hybrid constraint handling strategies, the proposed framework integrates exploration-exploitation balancing, constraint management, and predictive host utilization modeling into a unified scheduling architecture. The methodology emphasizes theoretical robustness by combining adaptive load prediction mechanisms with hybrid swarm-based search strategies for global optimization. Results are analyzed through extensive descriptive evaluation across heterogeneous task distributions, demonstrating improvements in workload balancing, latency reduction, and sustained host utilization stability compared to classical scheduling paradigms such as Shortest Job First and static heuristic allocation. The discussion elaborates on algorithmic convergence behavior, scalability, constraint sensitivity, and enterprise-level applicability. The findings contribute to the theoretical consolidation of hybrid metaheuristic strategies in cloud computing and provide a scalable architectural foundation for next-generation intelligent cloud resource orchestration systems.