A container optimal matching deployment algorithm based on CN-Graph for mobile edge computing

Huanle Rao, Sheng Chen, Yuxuan Du, Xiaobin Xu, Haodong Chen, Gangyong Jia
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Abstract

The deployment of increasingly diverse services on edge devices is becoming increasingly prevalent. Efficiently deploying functionally heterogeneous services to resource heterogeneous edge nodes while achieving superior user experience is a challenge that every edge system must address. In this paper, we propose a container-node graph (CN-Graph)-based container optimal matching deployment algorithm, edge Kuhn-Munkres algorithm (EKM) based on container-node graph, designed for heterogeneous environment to optimize system performance. Initially, containers are categorized by functional labels, followed by construction of a CN-Graph model based on the relationship between containers and nodes. Finally, the container deployment problem is transformed into a weighted bipartite graph optimal matching problem. In comparison with the mainstream container deployment algorithms, Swarm, Kubernetes, and the recently emerged ECSched-dp algorithm, the EKM algorithm demonstrates the ability to effectively enhance the average runtime performance of containers to 3.74 times, 4.10 times, and 2.39 times, respectively.

Abstract Image

基于 CN-Graph 的移动边缘计算容器优化匹配部署算法
在边缘设备上部署日益多样化的服务正变得越来越普遍。如何将功能异构的服务高效地部署到资源异构的边缘节点上,同时实现卓越的用户体验,是每个边缘系统必须应对的挑战。本文提出了一种基于容器-节点图(CN-Graph)的容器优化匹配部署算法,即基于容器-节点图的边缘库恩-蒙克雷斯算法(EKM),旨在异构环境中优化系统性能。首先,根据功能标签对容器进行分类,然后根据容器和节点之间的关系构建 CN-Graph 模型。最后,容器部署问题被转化为加权双向图最优匹配问题。与主流的容器部署算法Swarm、Kubernetes和最近出现的ECSched-dp算法相比,EKM算法能有效提高容器的平均运行性能,分别达到3.74倍、4.10倍和2.39倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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