The exponential growth of data-intensive applications and the rapid proliferation of Internet of Things (IoT) devices have transformed the landscape of cloud computing into a multi-tiered, heterogeneous environment. Central to the efficiency of these systems is task scheduling-the process of mapping workloads to computational resources to satisfy conflicting objectives such as makespan minimization, cost-efficiency, and energy conservation. This research provides a deep theoretical and empirical investigation into the evolution of scheduling paradigms, ranging from traditional heuristic list scheduling to advanced hybrid meta-heuristics. We specifically evaluate the performance of bio-inspired algorithms, including Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithms (GA), in complex multi-cloud and fog environments. A primary focus is placed on the newly developed Hybrid Grey Wolf Whale Optimization (GWW-WO) model and its efficacy in dynamic resource distribution. By analyzing a wide array of taxonomies and systematic reviews, this study identifies critical challenges in Quality of Service (QoS) awareness and load balancing. The results underscore that hybrid approaches, which integrate the local search capabilities of heuristics with the global exploration of meta-heuristics, offer superior resilience in green cloud computing and distributed stream processing. The article concludes with a strategic roadmap for future research in autonomous, cost-aware scheduling for fog-integrated IoT applications.

Cloud Computing, Task Scheduling, Bio-inspired Meta-heuristics, Heterogeneous Systems

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