GACO: Resource Aware Scheduling in Multi Cloud Environment using Hybrid Meta-Heuristic Algorithm

A significant issue with multi-cloud environments is the continued complexity that arises from the dynamic and heterogeneous scheduling of tasks across clouds because multi-clouds have large variability in terms of their used resources. In this paper we propose a new paradigm for Task Scheduling (TS) for multi cloud environment using Genetic Ant Colony Optimization (GACO), a hybrid algorithm that synergizes the exploration capabilities of Genetic Algorithm (GA) with the exploitation strengths of ACO. The GACO algorithm aims to optimize task allocation by balancing multiple objectives, including minimizing makespan, reducing energy consumption, and lowering overall operational costs. By leveraging the global search properties of GA and the local optimization capabilities of ACO, GACO effectively navigates the complex search space to find near-optimal scheduling solutions. Extensive simulations conducted in diverse multi-cloud scenarios demonstrate that GACO outperforms traditional scheduling algorithms, leading to significant improvements in resource utilization, energy efficiency, and overall system performance. This work contributes to the field of Cloud Computing (CC) by providing a robust and scalable solution for TS in multi-cloud environments, ensuring efficient resource allocation and enhancing the quality of service.