Research and Implementation of Scheduling Strategy in Kubernetes for Computer Science Laboratory in Universities

Abstract

How to design efficient scheduling strategy for different environments is a hot topic in cloud computing. In the private cloud of computer science labs in universities, there are several kinds of tasks with different resource requirements, constraints, and lifecycles such as IT infrastructure tasks, course design tasks submitted by undergraduate students, deep learning tasks and and so forth. Taking the actual needs of our laboratory as an instance, these tasks are analyzed, and scheduled respectively by different scheduling strategies. The Batch Scheduler is designed to process tasks in rush time to improve system throughput. Dynamic scheduling algorithm is proposed to tackle long-term lifecycle tasks such as deep learning tasks which are hungry for GPU resources and have dynamically changing priorities. Experiments show that the scheduling strategies proposed in this paper improve resource utilization and efficiency.

Custom Scheduling in Kubernetes: A Survey on Common Problems and Solution Approaches

“…Evaluations using a real Kubernetes cluster show that using Kubesphere allows users to experience less waiting times on average. The authors in [113] propose two schedulers to place heterogeneous tasks in a computer science lab cluster. The irst one is a batch scheduler to be used in busy hours.…”

“…Discussion: As it can be noted from the aforementioned contributions, a common feature needed to support batch scheduling is the customization of the queue sorting behavior. This can be done by prioritizing users' workloads based on their resources requirements [12,113] or by organizing the queue into a hierarchical structure and using multiple policies for managing it [9]. As it can be seen in Table 7, the works reviewed in this section do not leverage the scheduling framework.…”