  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.
