## 1 Introduction  
A new concept called \"Knowledge Plane for Internet\" was given by [PERSON] et al. [1], which involves machine learning to operate the networks. According this concept, the Knowledge plane (KP) is considered as the potential candidate for computer network reforms, as it can bring automation and recommendation to the existing networks and changes the way we trouble shoot these data networks. One of the biggest challenges encountered when applying machine learning to the traditional networks is their inherently distributed nature, i.e., each and every node (switches and routers) can only see a small portion of a network. Therefore, it is a very complex job to obtain knowledge from the nodes that have very little knowledge about the whole network, particularly when the global control over the network is required. The emerging trend that promises logical centralized control will simplify the application of ML in traditional distributed setting. In particular, the Software Defined Networking architecture entirely separates the two planes, namely, the data and control plane, and provides the logical central control plane which can be considered as central station in the network with knowledge [2; 3]. Newer data plane elements (switches and routers) are now equipped with better computing and storage capabilities, hence providing packet and flow information in the real time to the centralized Network Analytics (NA), which enables the NA platform to provide a very rich view of the network [4], which gives it an edge over the traditional network management techniques. In this work, we take advantage of the centralized control offered by SDN, combined with the information provided by the NA about the network, to help us apply ML [1; 5]. In this context, the knowledge plane can benefit from various machine learning techniques already available in the literature to control the network [6]. In the rest of the paper, the SDN combined with network analytics and KP will be referred to as SDN-with-knowledge [3]. Recently, a similar architecture inspired from the knowledge plane [1] called \"Knowledge Defined Networking\" is proposed by [PERSON] et al. [7], which explains how such an ecosystem can be exploited with the help of ML to provide automated control to the network. The architecture proposed by [PERSON] et al. further investigated the allocation of resources in Network Function Virtualization (NFV) [7]. NFV is an emerging paradigm which separates the network functions (such as firewalls, proxies, load balancers, etc.) from the physical hardware on which they are executed. These network functions which are decoupled from physical hardware are called virtual network functions, which now require virtual resources (Virtual Machines (VMs)) to execute [8; 9]. Allocation of resources in NFV is also investigated by [10; 11]. Management of resources in NFV is most challenging because of the limited resources available for the VNFs [12; 13]. In static networks, the ideal placement of VNFs has been comprehensively studied [10]. However, the dynamic network environment requires advance algorithms to effectively allocate resources to VNFs, so it can deal the fluctuating real time traffic. However, VNFs are complex and dynamic entities, therefore accurate modeling of its behavior is really challenging task. In this context, ML models are very encouraging to solve this problem, which is briefly addressed in [7], but the area still has a lot of potential and requires in-depth research. The contributions of this paper are summarized as follow:  
1. A comprehensive survey has been carried out for existing ML techniques used for resource management in NFV environment. 2. The experiments are carried out using a real traffic trace, to first validate the data set already published by [7]. 3. Based on experimental testing and related literature, we have modeled multiple linear regression and support-vector-machine-based regression model to optimally estimate the CPU consumption of different VNFs. 4.