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arXiv 1807.02307cs.NI
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z-TORCH:一种自动化的NFV演示和监控解决方案

z-TORCH: An Automated NFV Orchestration and Monitoring Solution

Vincenzo Sciancalepore, Faqir Zarrar Yousaf, Xavier Costa-Perez

摘要:

虚拟化资源和服务的自主管理和编排(MANO),特别是在大规模网络功能虚拟化(NFV)环境中,由于各种网络服务所期望的严格的延迟和性能要求,是一个很大的挑战。管理和监控(MANO)系统的决策质量(QoD)取决于从底层监控系统接收到的信息的质量和及时性。监测系统生成的数据是MANO系统网络和处理负载的重要贡献者,从而影响其性能。这提出了一个独特的挑战:如何联合优化MANO系统的QoD,同时最大限度地减少运行时的监控负载?这是本文的主要内容。在这种情况下,我们提出了一种新的自动化NFV编排解决方案,即z-TORCH(零接触编排),它通过利用基于机器学习的技术来联合优化编排和监控过程。我们的目标是提高MANO系统的QoD,以最小的监控成本实现虚拟化网络功能(VNF)的接近最佳的位置。

英文摘要:

Autonomous management and orchestration (MANO) of virtualized resources and services, especially in large-scale Network Function Virtualization (NFV) environments, is a big challenge owing to the stringent delay and performance requirements expected of a variety of network services. The Quality-of-Decisions (QoD) of a Management and Orchestration (MANO) system depends on the quality and timeliness of the information received from the underlying monitoring system. The data generated by monitoring systems is a significant contributor to the network and processing load of MANO systems, impacting thus their performance. This raises a unique challenge: how to jointly optimize the QoD of MANO systems while at the same minimizing their monitoring loads at runtime? This is the main focus of this paper. In this context, we propose a novel automated NFV orchestration solution, namely z-TORCH (zero Touch Orchestration) that jointly optimizes the orchestration and monitoring processes by exploiting machine-learning-based techniques. The objective is to enhance the QoD of MANO systems achieving a near-optimal placement of Virtualized Network Functions (VNFs) at minimum monitoring costs.

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。

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