Efficient resource allocation in heterogeneous clouds: genetic water evaporation optimization for task scheduling

Cloud computing is a dynamic technology that requires efficient resource allocation strategies, and the task scheduling is optimized in heterogeneous cloud environments. The virtualization technology is largely responsible for the popularity of the cloud. During peak load periods, due to resource scarcity, some tasks are migrated to other data centers to achieve balance. To tackle this challenge, a novel method is proposed such as the genetic water evaporation optimization (GWEO) algorithm to perform an effective resource allocation in diverse cloud infrastructures. In order to reduce task execution time, the GWEO is associated with genetic algorithms and water evaporation optimization algorithm that maximize resource allocation. The scalability and adaptability of novel GWEO were explored through experiments involving larger-scale cloud deployments, showcasing its efficacy in handling complex scheduling tasks in real-world cloud environments. The promising results indicate its potential to significantly enhance performance and scalability in diverse cloud computing environments, addressing critical challenges associated with resource optimization and task scheduling.