Hybrid ACO-SA Algorithm for Bi-Objective Multi-Workflow Scheduling in Amazon Cloud Environments

In cloud computing systems, efficient workflow scheduling is essential for maximizing resource usage, reducing operating expenses, and achieving performance goals. Conventional scheduling algorithms sometimes fail to achieve a balance between conflicting goals, such lowering cost and makespan (total execution time), especially in settings with many workflows, complicated task dependencies, and fluctuating resource needs. In order to overcome these difficulties in AWS cloud settings, this study presents a unique method that combines Ant Colony Optimization (ACO) with Simulated Annealing (SA) in a hybrid algorithm.In order to accomplish bi-objective optimization, the suggested hybrid ACO-SA algorithm makes use of both SA's probabilistic acceptance method and ACO's worldwide exploration capabilities. Through the integration of various strategies, the algorithm efficiently navigates the multi-dimensional search space, providing solutions that strike a compromise between cost-effectiveness and makespan. The technique is implemented and scaled using AWS services including Lambda, CodePipeline, S3, and Amazon EC2, which guarantees flexibility and resilience in managing a range of workflow scenarios. the algorithm's superiority over traditional methods in terms of makespan reduction, cost efficiency, and adherence to deadlines. The findings underscore its potential for enhancing operational efficiency and scalability in real-world cloud computing applications. This study contributes to advancing scheduling techniques in cloud environments and suggests avenues for further research in dynamic adaptation and integration with advanced machine learning techniques.