{"title":"评估边缘计算中集群间通信的影响","authors":"Marc Michalke, Iulisloi Zacarias, Admela Jukan","doi":"arxiv-2409.09278","DOIUrl":null,"url":null,"abstract":"Distributed applications based on micro-services in edge computing are\nbecoming increasingly popular due to the rapid evolution of mobile networks.\nWhile Kubernetes is the default framework when it comes to orchestrating and\nmanaging micro-service-based applications in mobile networks, the requirement\nto run applications between multiple sites at cloud and edge poses new\nchallenges. Since Kubernetes does not natively provide tools to abstract\ninter-cluster communications at the application level, inter-cluster\ncommunication in edge computing is becoming increasingly critical to the\napplication performance. In this paper, we evaluate for the first time the\nimpact of inter-cluster communication on edge computing performance by using\nthree prominent, open source inter-cluster communication projects and tools,\ni.e., Submariner, ClusterLink and Skupper. We develop a fully open-source\ntestbed that integrates these tools in a modular fashion, and experimentally\nbenchmark sample applications, including the ML class of applications, on their\nperformance running in the multi-cluster edge computing system under varying\nnetworking conditions. We experimentally analyze two classes of envisioned\nmobile applications, i.e., a) industrial automation, b) vehicle decision drive\nassist. Our results show that Submariner performs best out of the three tools\nin scenarios with small payloads, regardless of the underlying networking\nconditions or transmission direction between clusters. When sending larger data\nto a service, ClusterLink outperforms Submariner once the inter-node networking\nconditions deteriorate, which may be the case in highly mobile scenarios in\nedge computing. Finally, Skupper significantly outperforms others in a variety\nof scenarios with larger payloads.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":"195 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Evaluating the Impact of Inter-cluster Communications in Edge Computing\",\"authors\":\"Marc Michalke, Iulisloi Zacarias, Admela Jukan\",\"doi\":\"arxiv-2409.09278\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Distributed applications based on micro-services in edge computing are\\nbecoming increasingly popular due to the rapid evolution of mobile networks.\\nWhile Kubernetes is the default framework when it comes to orchestrating and\\nmanaging micro-service-based applications in mobile networks, the requirement\\nto run applications between multiple sites at cloud and edge poses new\\nchallenges. Since Kubernetes does not natively provide tools to abstract\\ninter-cluster communications at the application level, inter-cluster\\ncommunication in edge computing is becoming increasingly critical to the\\napplication performance. In this paper, we evaluate for the first time the\\nimpact of inter-cluster communication on edge computing performance by using\\nthree prominent, open source inter-cluster communication projects and tools,\\ni.e., Submariner, ClusterLink and Skupper. We develop a fully open-source\\ntestbed that integrates these tools in a modular fashion, and experimentally\\nbenchmark sample applications, including the ML class of applications, on their\\nperformance running in the multi-cluster edge computing system under varying\\nnetworking conditions. We experimentally analyze two classes of envisioned\\nmobile applications, i.e., a) industrial automation, b) vehicle decision drive\\nassist. 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引用次数: 0
摘要
虽然 Kubernetes 是在移动网络中协调和管理基于微服务的应用的默认框架,但在云和边缘的多个站点之间运行应用的要求带来了新的挑战。由于 Kubernetes 本身不提供在应用层抽象集群间通信的工具,因此边缘计算中的集群间通信对应用性能的影响变得越来越关键。在本文中,我们利用三个著名的开源集群间通信项目和工具(即 Submariner、ClusterLink 和 Skupper),首次评估了集群间通信对边缘计算性能的影响。我们开发了一个完全开源的测试平台,以模块化方式集成了这些工具,并对样本应用(包括 ML 类应用)在不同网络条件下在多集群边缘计算系统中的运行性能进行了实验基准测试。我们对两类设想的移动应用进行了实验分析,即 a) 工业自动化;b) 车辆决策驱动辅助。我们的结果表明,无论底层网络条件或集群间的传输方向如何,Submariner 在有效载荷较小的应用场景中都是三种工具中表现最好的。当向服务发送较大数据时,一旦节点间网络条件恶化,ClusterLink 的性能就会优于 Submariner,在边缘计算的高移动性场景中可能会出现这种情况。最后,在有效载荷较大的各种场景中,Skupper 的性能明显优于其他产品。
Evaluating the Impact of Inter-cluster Communications in Edge Computing
Distributed applications based on micro-services in edge computing are
becoming increasingly popular due to the rapid evolution of mobile networks.
While Kubernetes is the default framework when it comes to orchestrating and
managing micro-service-based applications in mobile networks, the requirement
to run applications between multiple sites at cloud and edge poses new
challenges. Since Kubernetes does not natively provide tools to abstract
inter-cluster communications at the application level, inter-cluster
communication in edge computing is becoming increasingly critical to the
application performance. In this paper, we evaluate for the first time the
impact of inter-cluster communication on edge computing performance by using
three prominent, open source inter-cluster communication projects and tools,
i.e., Submariner, ClusterLink and Skupper. We develop a fully open-source
testbed that integrates these tools in a modular fashion, and experimentally
benchmark sample applications, including the ML class of applications, on their
performance running in the multi-cluster edge computing system under varying
networking conditions. We experimentally analyze two classes of envisioned
mobile applications, i.e., a) industrial automation, b) vehicle decision drive
assist. Our results show that Submariner performs best out of the three tools
in scenarios with small payloads, regardless of the underlying networking
conditions or transmission direction between clusters. When sending larger data
to a service, ClusterLink outperforms Submariner once the inter-node networking
conditions deteriorate, which may be the case in highly mobile scenarios in
edge computing. Finally, Skupper significantly outperforms others in a variety
of scenarios with larger payloads.