Efficient Container Assignment and Layer Sequencing in Edge Computing

Containers are becoming a popular way of running applications in edge computing. Before running the application, the edge node must download the application’s container image consisting of multiple layers. However, given the limited bandwidth in edge computing, the container startup latency due to long image download time seriously affects the real-time performance. In this article, we jointly determine the container assignment and the layer download sequence to reduce the total startup latency. We formulate the Container Assignment and Layer Sequencing (CALS) problem and prove its NP-hardness. A Layer-Aware Scheduling Algorithm (LASA) is proposed, fully considering layer sharing among images. First, layers shared by the same set of images are grouped to reduce CALS’s problem scale without affecting the optimal result. Second, considering both layer sharing and existing layer size on edge nodes, a layer-aware algorithm is designed to assign containers to appropriate edge nodes. Finally, to determine the layer download sequence on each edge node, an approximation algorithm is proposed. We further analyze the approximation ratio of LASA in the case of identical edge nodes with sufficient capacity. Extensive experiments based on real-world data show the effectiveness of LASA, which reduces the total startup latency by 40% to 60%.

1 Introduction

With the increasing demand for low-latency and highly flexible applications, cloudlets [1], fog [2] and edge computing [3] that are in closer proximity to mobile devices provide attractive ways to deploy applications. Ultra-low latency and ultra-high bandwidth 5G technology further facilitates the development of edge computing [4], [5]. Virtualization can provide isolated environments for applications to avoid software-dependency conflicts and enhance system robustness [6]. However, in edge computing, the computation resources and communication resources are limited compared with the cloud, and the edge environment changes are rapid [7], [8]. Traditional virtualization techniques, i.e., heavy virtual machine (VM), cannot resolve these issues. The emerging technique, container, is believed to be a promising way to deploy applications in edge computing [9], [10]. Multiple containers on the same node share the machine’s OS system kernel and thereby do not require an OS per container, driving higher server efficiencies, suitable for resource-limited edge nodes.