Task Scheduling with Optimized Load Balancing in Container-Based Cloud Computing Environments

Challenge of Task Scheduling and Load Balancing Task scheduling and load balancing are the main challenges supported by the dynamic nature of cloud computing environments based on containers. While container-based cloud computing provides a flexible and economical way to deploy multiple applications and services, managing tasks and resources correctly is important for the best performance/cost. This paper proposes an approach to task scheduling problem with optimized load balancing in containerized cloud environments. We consider the availability of resources and complexity in tasks, dynamically balancing workload on containers to ensure work distribution across all nodes. This work uses genetic algorithms and reinforcement learning methods to search for efficient task allocation strategies. Our experiments validate our technique by demonstrating effective scheduling at the whole-system level and a lower resource waste than traditional methods. Our solution also works in container-based cloud computing environments with dynamically changing workloads and resources. Our proposed method can be a good strategy for effective task assignments and resources in container-based cloud computing environments.