Scheduling is an NP-hard problem, and heuristic algorithms are unable to find approximate solutions within a feasible time frame. In Cloud Computing (CC) environments, efficient Task Scheduling (TS) plays a critical role in minimizing operational expenses and enhancing system reliability. This paper presents a novel task scheduling approach that uses the Coati Optimization Algorithm (COA) to address two pivotal challenges: reducing the total cost (sum of computational cost and communication cost) and minimizing Virtual Machine (VM) failure rates. Inspired by the cooperative foraging and adaptive behavior of coatis in dynamic environments, the proposed algorithm leverages intelligent exploration and exploitation strategies to identify optimal task-to-VM mappings under fluctuating workloads. The COA incorporates cost-awareness and failure probability metrics into its fitness function to ensure robust scheduling decisions that align with budgetary constraints and fault tolerance requirements. To assess the performance of the proposed model, comprehensive simulations were conducted using the CEA-Curie real-world workload. The results were compared against three state-of-the-art approaches, MoHHOTS, RTATSA2C, and TS-GWO. Experimental evaluations demonstrate that COA significantly outperforms these existing methods by achieving a 19.8% reduction in overall cost and a 22.5% decrease in VM failure rate. These findings demonstrate that COA offer a promising pathway toward sustainable, cost-effective, and resilient task execution in large-scale cloud infrastructures, particularly under diverse and realistic workload scenarios.

Scheduling in cloud computing is an NP-hard problem, where traditional metaheuristic algorithms often fail to deliver approximate solutions within a feasible time frame. As cloud infrastructures become increasingly dynamic, efficient Task Scheduling (TS) remains a major challenge, especially when minimizing makespan, execution time, and resource utilization. To address this, we propose the Ant Colony Asynchronous Advantage Actor-Critic (A4C) algorithm, which synergistically combines the exploratory strengths of Ant Colony Optimization (ACO) with the adaptive learning capabilities of the Asynchronous Advantage Actor-Critic (A3C) model. While ACO efficiently explores task allocation paths, it is prone to getting trapped in local optima. The integration with A3C overcomes this limitation by leveraging deep reinforcement learning for real-time policy and value estimation, enabling adaptive and informed scheduling decisions. Extensive simulations show that the A4C algorithm improves throughput by 18.7%, reduces makespan by 16%, execution time by 14.60%, and response time by 21.4% compared to conventional approaches. These results validate the practical effectiveness of A4C in handling dynamic workloads, reducing computational overhead, and ensuring timely task completion. The proposed model not only enhances scheduling efficiency but also supports quality-driven service delivery in cloud environments, making it well-suited for managing complex and time-sensitive cloud applications.