LRScheduler: A Layer-aware and Resource-adaptive Container Scheduler in Edge Computing
Zhiqing Tang, Wentao Peng, Jianxiong Guo, Jiong Lou, Hanshuai Cui, Tian Wang, Yuan Wu, Weijia Jia

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
