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Autonomous management and orchestration (MANO) of virtualized resources andservices, especially in large-scale Network Function Virtualization (NFV)environments, is a big challenge owing to the stringent delay and performancerequirements expected of a variety of network services. TheQuality-of-Decisions (QoD) of a Management and Orchestration (MANO) systemdepends on the quality and timeliness of the information received from theunderlying monitoring system. The data generated by monitoring systems is asignificant contributor to the network and processing load of MANO systems,impacting thus their performance. This raises a unique challenge: how tojointly optimize the QoD of MANO systems while at the same minimizing theirmonitoring 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 theorchestration and monitoring processes by exploiting machine-learning-basedtechniques. The objective is to enhance the QoD of MANO systems achieving anear-optimal placement of Virtualized Network Functions (VNFs) at minimummonitoring costs.

z-TORCH: An Automated NFV Orchestration and Monitoring Solution