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The emergency of machine type and ultra-reliable low latency communication is imposing stringent constraints for service provisioning. Addressing such constraints is challenging for network and cloud service providers. As a trending paradigm, software-defined networking (SDN) plays a significant role in future networks and services. However, the classical implementation of the SDN controller has limitations in-terms-of latency and reliability since the controller is decoupled from the forwarding device. Several research works have tried to tackle these challenges by proposing solutions such as Devoflow, DIFANE, and hierarchical and distributed controller deployment. Nonetheless, these approaches are not fully addressing these challenges. This paper tries to address the problem of latency and reliability by proposing a dynamic controller role delegation architecture for forwarding devices. To align with the microservice or multi-agent-based service-based architecture, the role delegation function as a service is proposed. The dynamic role delegation enables to predict and (pre-)installed flow rules in the forwarding devices based on various considerations such as network state, packet type, and service's stringent requirements. The proposed architecture is implemented and evaluated for latency and resiliency performance in comparison to the centralized and distributed deployment of the SDN controller. We used ComNetsEmu, a softwarized network emulation tool, to emulate SDN and NFV (Network Function Virtualization). The result indicated a significant decrease in latency and improved resilience in case of failure, yielding better network performance. 

The continuous increase in demanding for availability and ultra-reliability of low-latency and broadband wireless connections is instigating further research in the standardization of next-generation mobile systems. 6G networks, among other benefits, should offer global ubiquitous mobility thanks to the utilization of the Space segment as an intelligent yet autonomous ecosystem. In this framework, multi-layered networks will take charge of providing connectivity by implementing Cloud-Radio Access Network (C-RAN) functionalities on heterogeneous nodes distributed over aerial and orbital segments. Unmanned Aerial Vehicles (UAVs), High-Altitude Plat-forms (HAPs), and small satellites compose the Space ecosystem encompassing the 3D networks. Recently, a lot of interest has been raised about splitting operations to distribute baseband processing functionalities among such nodes to balance the computational load and reduce the power consumption. This work focuses on the hardware development of C-RAN physical (PHY-) layer operations to derive their computational and energy demand. More in detail, the 5G Downlink Shared Channel (DLSCH) and the Physical Downlink Shared Channel (PDSCH) are first simulated in MATLAB environment to evaluate the variation of computational load depending on the selected splitting options and number of antennas available at transmitter (TX) and receiver (RX) side. Then, the PHY-layer processing chain is software-implemented and the various splitting options are tested on low-cost processors, such as Raspberry Pi (RP) 3B+ and 4B. By overclocking the RPs, we compute the execution time and we derive the instruction count (IC) per program for each considered splitting option so to achieve the mega instructions per second (MIPS) for the expected processing time. Finally, by comparing the performance achieved by the employed RPs with that of Nvidia Jetson Nano (JN) processor used as benchmark, we shall discuss about size, weight, power and cost (SWaP-C)... 

Recently, a multi-agent based network automation architecture has been proposed. The architecture is named multi-agent based network automation of the network management system (MANA-NMS). The architectural framework introduced atomized network functions (ANFs). ANFs should be autonomous, atomic, and intelligent agents. Such agents should be implemented as an independent decision element, using machine/deep learning (ML/DL) as an internal cognitive and reasoning part. Using these atomic and intelligent agents as a building block, a MANA-NMS can be composed using the appropriate functions. As a continuation toward implementation of the architecture MANA-NMS, this paper presents a network traffic prediction agent (NTPA) and a network traffic classification agent (NTCA) for a network traffic management system. First, an NTPA is designed and implemented using DL algorithms, i.e., long short-term memory (LSTM), gated recurrent unit (GRU), multilayer perceptrons (MLPs), and convolutional neural network (CNN) algorithms as a reasoning and cognitive part of the agent. Similarly, an NTCA is designed using decision tree (DT), K-nearest neighbors (K-NN), support vector machine (SVM), and naive Bayes (NB) as a cognitive component in the agent design. We then measure the NTPA prediction accuracy, training latency, prediction latency, and computational resource consumption. The results indicate that the LSTM-based NTPA outperforms compared to GRU, MLP, and CNN-based NTPA in terms of prediction accuracy, and prediction latency. We also evaluate the accuracy of the classifier, training latency, classification latency, and computational resource consumption of NTCA using the ML models. The performance evaluation shows that the DT-based NTCA performs the best.