ROMA：基于微服务的5G应用的资源编排
ROMA: Resource Orchestration for Microservices-based 5G Applications
摘要：
随着5G、物联网（IoT）、边缘计算和云计算技术的发展，新兴应用（AR/VR、自动驾驶、工业4.0等）可用的基础设施（计算和网络）变得相当复杂存在多个计算层（IoT设备、近边缘、远边缘、云等）。它们与不同类型的网络技术（LAN、LTE、5G、MAN、WAN等）连接。在这样的环境中部署和管理应用程序是非常具有挑战性的。在本文中，我们提出了ROMA，它在动态，异构，多层计算和网络结构中为基于微服务的5G应用程序执行资源编排。我们假设只有应用程序级别的需求是已知的，而应用程序中各个微服务的详细需求没有指定。作为我们解决方案的一部分，ROMA识别并利用各种微服务的计算和网络使用之间的耦合关系，并解决优化问题，以适当地识别每个微服务应如何部署在复杂的多层计算和网络结构中，从而最佳地满足端到端应用程序的需求。我们在视频监控和智能交通系统（ITS）领域实现了两个真实世界的5G应用。通过大量的实验，我们表明，ROMA能够节省高达90%，55%和44%的计算和高达80%，95%和75%的网络带宽的监控（监视列表）和交通应用（人和汽车检测），分别。这种改进是在满足应用程序性能要求的同时实现的，并且它优于通过忽略资源耦合关系而采用静态和过度供应的资源分配策略的替代方案。
英文摘要：
With the growth of 5G, Internet of Things (IoT), edge computing and cloud computing technologies, the infrastructure (compute and network) available to emerging applications (AR/VR, autonomous driving, industry 4.0, etc.) has become quite complex. There are multiple tiers of computing (IoT devices, near edge, far edge, cloud, etc.) that are connected with different types of networking technologies (LAN, LTE, 5G, MAN, WAN, etc.). Deployment and management of applications in such an environment is quite challenging. In this paper, we propose ROMA, which performs resource orchestration for microservices-based 5G applications in a dynamic, heterogeneous, multi-tiered compute and network fabric. We assume that only application-level requirements are known, and the detailed requirements of the individual microservices in the application are not specified. As part of our solution, ROMA identifies and leverages the coupling relationship between compute and network usage for various microservices and solves an optimization problem in order to appropriately identify how each microservice should be deployed in the complex, multi-tiered compute and network fabric, so that the end-to-end application requirements are optimally met. We implemented two real-world 5G applications in video surveillance and intelligent transportation system (ITS) domains. Through extensive experiments, we show that ROMA is able to save up to 90%, 55% and 44% compute and up to 80%, 95% and 75% network bandwidth for the surveillance (watchlist) and transportation application (person and car detection), respectively. This improvement is achieved while honoring the application performance requirements, and it is over an alternative scheme that employs a static and overprovisioned resource allocation strategy by ignoring the resource coupling relationships.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
