LRScheduler: A Layer-aware and Resource-adaptive Container Scheduler in Edge Computing
Journal:
arXiv
Published Date:
Jun 4, 2025
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.
