DSTS: A hybrid optimal and deep reinforcement learning for dynamic scalable task scheduling on container cloud environment

Abstract

Containers have grown into the most dependable and lightweight virtualization platform for delivering cloud services, offering flexible sorting, portability, and scalability. In cloud container services, planner components play a critical role. This enhances cloud resource workloads and diversity performance while lowering costs. We present a hybrid optimum and deep reinforcement learning approach for dynamic scalable task scheduling (DSTS) in a container cloud environment in this research. To expand containers virtual resources, we first offer a modified multi-swarm coyote optimization (MMCO) method, which improves customer service level agreements. Then, to assure priority-based scheduling, we create a modified pigeon-inspired optimization (MPIO) method for task clustering and a rapid adaptive feedback recurrent neural network (FARNN) for pre-virtual CPU allocation. Meanwhile, the task load monitoring system is built on a deep convolutional neural network (DCNN), which allows for dynamic priority-based scheduling. Finally, the presentation of the planned DSTS methodology will be estimated utilizing various test vectors, and the results will be associated to present state-of-the-art techniques.