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

Abstract

Since its release in 2014, Kubernetes has become a popular choice for orchestrating containerized workloads at scale. In order to determine the most appropriate node to host a given workload, Kubernetes makes use of a scheduler that takes into account a set of hard and soft constraints defined by workload owners and cluster administrators. Despite being highly configurable, the default Kubernetes scheduler cannot fully meet the requirements of emerging applications, such as machine/deep learning workloads and edge computing applications. This has led to different proposals of custom Kubernetes schedulers that focus on addressing the requirements of the aforementioned applications. Since the related literature is growing in this area, we aimed, in this survey, to provide a classification of the related literature based on multiple criteria, including scheduling objectives as well as the types of considered workloads and environments. Additionally, we provide an overview of the main approaches that have been adopted to achieve each objective. Finally, we highlight a set of gaps that could be leveraged by the academia or industry to drive further research and development activities in the area of custom scheduling in Kubernetes.

A Framework for Cognitive, Decentralized Container Orchestration

“…Current scheduling solutions are also limited by the lack of network awareness in scheduling decisions [56]. The K8s network-aware scheduler plugin addresses this issue by enabling latency-and bandwidth-aware pod scheduling that considers both the application and infrastructure network topology.…”

Optimization of Task-Scheduling Strategy in Edge Kubernetes Clusters Based on Deep Reinforcement Learning

“…The scheduler is pluggable and can be customized or extended as per user requirements. Kubernetes provides various ways to implement custom schedulers such as modifying the Kube-scheduler source code, implementing extended schedulers, or creating custom score plugins in the scheduling framework [20,21].…”

Dynamic, Context-Aware Cross-Layer Orchestration of Containerized Applications

“…Supporting elasticity as well as internal and external system dynamics are fundamental requirements in Edge-Cloud environments, and therefore offering appropriate resource scaling is one of the most important features. Adopting AI/ML techniques in the orchestration process will ensure better adaptability, as it will increase the overall operational efficiency and provide more flexibility by replacing the manual configuration with digital intelligence, reducing the need for manual resource monitoring, tracking of data usage, calculating the optimal configurations, and changing the configurations accordingly [37]. These tasks are automated and become routine when accomplished using AI/ML techniques in the orchestration process.…”