## 1 Introduction  
Traditional networks consist of several devices, including switches, routers, servers, deep packet inspection, firewalls, and hosts. These devices are traditionally implemented using hardware. A human administrator manages such small networks [1, 2], through configuration and management interfaces. Typically, an administrator is necessary to configure and make the required changes. This is feasible only for relatively small size networks, albeit with limited performance and flexibility in terms of resource utilization, energy efficiency, service admission, latency, reliability, etc. Nevertheless, within the last two decades, communication networks have tremendously evolved. It is now possible to interconnect a massive number of devices and applications including traditional users, IoT devices, and machines. For instance, in the wireless domain, 4G and 5G have shown a multi-fold increase in the network size. The advent of IoT and other machine-to-machine communication paradigms also hugely increases the network size. The legacy network implementation has an enormous capital expenditure (CAPEX) since such networks are hardware-based and manually operated, which is inefficient and inflexible. As the network grows larger, it is increasingly difficult and expensive to manually administer its complexity, implying an increase in operational expenditure (OPEX). This is mainly because a massive number of technical personnel and experts is needed and sometimes humanly challenging and inefficient to perform, resulting in degrading network performance. Moreover, it is expected that Beyond 5G (B5G) and 6G will increase in size and become more complex. Furthermore, as indicated in 6G deliverable [3; 4], intelligence and autonomy are expected to be the core principles that drive the future networks, B5G and 6G. In this direction, an approach for in-network intelligence and automation is required to address the expected network growth as well as stringent requirements of various services. Currently, software-defined networking (SDN) and network function virtualization (NFV) are enabling network softwarization, subsequently increasing network flexibility and opening the door to autonomic networking. SDN provides a programmable network, while NFV softwarizes network functions, decoupling the hardware-based implementation to general-purpose and cloud-based deployment [5]. Autonomic networking is the process of enabling the network system to have a self-X property; X can be managing, configuring, healing, and protecting the network [6]. This was originally aimed at creating autonomic computing by IBM [1; 7]. One of the reasons for having automated network management systems is the idea of dynamic network resource allocation [8; 9] based on the service traffic arrival statistics. A self-managing network should be able to autonomously predict, diagnose, reconfigure, monitor, and circumvent failures while providing service processing as per the user traffic demands. This means, an autonomous network management system (ANMS) can collect, analyze, act, while adapting to the network and user dynamic and their evolving behavior [10]. Using ANMS, networks are capable of self-management and autonomous decisions both in unsupervised and supervised manner [11]. This guarantees a simplified control, management, and operation of complex and vast networks. These research works are a classic approach for network automation that we developed before the introduction of the softwarized networking, which transformed the network management, design and configurations. Recently, the MANA-NMS architectural framework was proposed in [12; 13], using a multi-agent system (MAS). Defining atomic decision-making units, the authors discussed the concept of network function atomization. Such atomic units could replace the virtual network functions (VNFS) with atomized intelligent agents. The approach is a service-oriented architectural (SOA) design for a flexible, loosely coupled, and intelligent system development for future generation networks.