LRScheduler: A Layer-aware and Resource-adaptive Container Scheduler in Edge Computing

TL;DR

LRScheduler is a practical container scheduler for edge computing that leverages layer sharing and resource adaptivity to reduce deployment costs and improve load balancing, built on Kubernetes.

Contribution

It introduces a layer-aware, resource-adaptive scheduling mechanism that enhances deployment efficiency and load balancing in edge environments, with real-system validation.

Findings

Reduces container deployment cost compared to default Kubernetes scheduler.

Improves load balancing by dynamically adjusting layer sharing scores.

Effectively utilizes idle resources in edge clusters.

Abstract

Lightweight containers provide an efficient approach for deploying computation-intensive applications in network edge. The layered storage structure of container images can further reduce the deployment cost and container startup time. Existing researches discuss layer sharing scheduling theoretically but with little attention paid to the practical implementation. To fill in this gap, we propose and implement a Layer-aware and Resource-adaptive container Scheduler (LRScheduler) in edge computing. Specifically, we first utilize container image layer information to design and implement a node scoring and container scheduling mechanism. This mechanism can effectively reduce the download cost when deploying containers, which is very important in edge computing with limited bandwidth. Then, we design a dynamically weighted and resource-adaptive mechanism to enhance load balancing in edge clusters, increasing layer sharing scores when resource load is low to use idle resources effectively. Our scheduler is built on the scheduling framework of Kubernetes, enabling full process automation from task information acquisition to container dep=loyment. Testing on a real system has shown that our design can effectively reduce the container deployment cost as compared with the default scheduler.