## 4 Implementation Scenario and Conceptual Framework
### Implementation Conceptual Framework for MANA-NMS Using NTCA  
Similar to the NTPA, we used the NTCA design guideline presented above, as depicted in Figure 5, for the agent implementation. Using the input traffic, NTCA classifies the incoming network traffic into the appropriate classes. NTCA helps with important decisions in network management, for instance, for QoS provisioning, resource provisioning, dynamic network slicing, etc. In performing autonomous service management and provisioning, such as resources provisioning and task scheduling, it is necessary to classify all incoming network traffic as per the protocol to which it belongs to. The protocols indicate the application or category that the traffic/user or service belongs to. The NTCA uses an ML algorithm as an internal cognitive part to perform the required traffic classification. Supervised ML algorithms are employed for the NTCA implementation. The supervised ML uses a labeled (protocols or port numbers) network traffic dataset. The supervised learning models used are K-NN, DT, SVM, and NB, and their performance is compared. We also assume that the NTCA can collect incoming network traffic and store it as a historic dataset. This dataset will be used by the agent to label and utilize it in training and re-training the internal cognitive component. The assumption is that the dataset will be stored in a centralized/distributed database: it could be internal or through the use of other database management agents. In our case, NTCA can handle the dataset and enable the dataset to retrieve it when needed. In the NTCA operation, the traffic dataset is collected using Wireshark in real time. About 100,000 entries are collected while surfing the internet for about an hour. The data are saved in CSV format and used in the feature extraction and feature selection stages. The extracted features are the source and destination IP address, packet size, and protocol. The main goal is to demonstrate the implementation of an NTCA before using it to recompose a MANA-NMS system. NTCA is a building block in the MANA-NMS framework as depicted in Figure 7. The NTCA collects the incoming network traffic and classifies them into three different classes. Similar to the NTPA implementation, we assumed high priority (HP), for uMTC service types, medium priority (MP) for mMTC service types, and low priority (LP) for xMBB service types. After the network traffic is classified, it is sent to an event handler (task manager/dispatcher). The event handler then decides where to schedule the traffic relaying based on the scheduling policy. A FIFO policy is used in the case of traffic belonging to the same classes. Then a task is scheduled onto the appropriate service agent to be executed. Figure 7: Implemented MANA-NMS scenario. 4.2.1. Job/Task/Event Handler and Queuing Scheduling  
A task (event) manager agent is another type of agent in a given MANA-NMS architecture. It receives the classified network traffic from NTCA. We assumed every class of the network traffic as a task requiring execution in the service processing agent. The task manager decides which service processing agent should handle the service. This decision is performed using a scheduling policy or intelligent scheduling agent. 4.2.2. Scheduling Policy  
\"Join Class-Related Queue\" is used for scheduling. Scheduling is a policy that sends tasks to the queues of the appropriate service processing agent. The agent should be subscribed to that particular task execution. For instance, when an HP class job is sent by the event manager, it will be placed in the queue of a particular service agent that is allocated to process an HP task. 4.2.3. Queuing Model Employed  
We assumed an M/M/1 Markovian model for the queue. The queuing policy is assumed to be FIFO similar to the NTPA scheduling policy. In general, M/M/1 means that the system has a Poisson arrival process with an arrival rate of \(\lambda\), an exponential service time distribution with service time \(\upmu\), and one server.