Meta-heuristic Algorithms to Optimize Two-Stage Task Scheduling in the Cloud

The development of cloud technology has led to more resources being made available on demand. The recent spike in the cloud service demand requires further improvement of cloud-based data centers. As a result, effective task scheduling is necessary for cloud computing. To ensure equal load distribution to systems with increased scalability and performance, data centers must have a suitable task scheduling mechanism. An efficient task scheduling strategy tries to optimize output, decrease response time, use fewer resources, and conserve energy by matching the appropriate resources to the workload. The suggested technique employs a two-stage task scheduling approach. In the first stage, virtual machines are created by performing classification and clustering techniques based on historical task data, and in the second stage, a hybrid ant genetic algorithm is used to schedule the best VM for the task by combining the advantages of genetic algorithms with pheromone values from ant colony algorithms. The suggested approach accomplished cost-effective task scheduling with a short make-span. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.