Wireless local area networks (WLAN) and recent Wi-Fi standards, are used since a few decades for a large variety of network applications. Since a few years, the $5^{\text {th }}$ generation wireless cellular networks (5 G) are being deployed and used, most of the time for similar applications. Both technologies and their evolution, are expected to be deployed within the wireless ecosystem by 2030 and for decades beyond. The Wi-FIP project invokes these two wireless networking solutions and their evolution, to further enhance the wireless data networks regarding sustainable and scalable quality of hybrid network services. This paper presents the visions and objectives of the Wi-FIP project, targeting to enhance synergies and integration of Wi-Fi in 5G/beyond 5G networks, through the convergent B2B/B2C usage model concept, sustainable multi-connectivity, hierarchical Software Defined Networking (SDN) and orchestration. The paper analyzes the functionalities to develop for sustainable, secure and hybrid WLAN/cellular networking.

The 5G/6G era has introduced a wide variety of services, including enhanced Mobile Broadband (eMBB), UltraReliable Low-Latency Communications (URLLC), and massive Machine-Type Communications (mMTC). Each service presents unique, highly diversified, and often conflicting requirements, driving the need for more flexible and intelligent solutions. In this context, Network Slicing (NS) has emerged as a prominent technology that allows multiple virtual networks to operate over a shared physical infrastructure, thereby accommodating these diverse service demands. Supported by technologies such as Software-Defined Networking (SDN) and Network Function Virtualization (NFV), network slicing requires the efficient placement of slices to optimize resource utilization and ensure Quality of Service (QoS). We propose a native artificial intelligence (AI) architecture for end-to-end (E2E) slicing that leverages Transformer-based Deep Reinforcement Learning (DRL) to enable zero-touch, automated slice placement in future networks, such as 5 G -and-beyond systems. Our system embeds AI directly into the network fabric, supporting native AI for real-time data processing and decision-making. Results show that integrating the Transformer model with DRL effectively addresses complex optimization challenges in network slicing, outperforming other state-of-the-art learning algorithms by better balancing slice acceptance ratio and energy efficiency. This supports the sustainable management of future networks, aligns with the vision of the Next Generation Mobile Networks (NGMN) Alliance, and illustrates the evolving role of AI in next-generation communication systems.

Although Network Function Virtualization (NFV) has introduced better flexibility and agility to the way network operators design, manage, and deploy their network services, it is still challenging to find the optimal real-time placement of network services which have evolved into complex dynamic graphs (or VNF-FGs) to satisfy the Quality of Service (QoS) requirements of their end-users and accommodate their dynamically changing service demands. Another crucial challenge that compounds the complexity of the online network service provisioning is to efficiently improve the utilization of the limited resources and reduce energy consumption and costs for service and infrastructure providers in large-scale networking environments such as 5G networks, edge computing, and Internet of Things (IoT). To meet both user and service provider needs, this paper proposes a novel Transformer-based Deep Reinforcement Learning (DRL) architecture, called TDRL (Transformer based-DRL), to address the dynamic energy-aware VNF-FG placement problem. Our intelligent encoder-decoder architecture leverages the power of both Graph Attention Networks (GAT) which extract the important features of the physical network, and sequence-to-sequence (seq2seq) models with Transformers which encode the ordered requirements of the complex VNF-FG service graphs. The main aim of these techniques is to improve the combined representation of the current state environment, and help our actor-critic DRL agent learn the optimal policy that achieves a "one-shot" placement decision of all VNFs in the service graph, thereby improving placement efficiency and resource utilization, especially in large-scale systems. Our extensive simulation results show that our TDRL approach significantly outperforms other state-of-the-art baseline learning algorithms in terms of achieving the optimal balance between acceptance ratio and energy efficiency.

We present a generic multi-agent deep reinforcement learning framework for dynamic multi-domain service provisioning in large-scale networks. We formulate both the assignment of a given sub-VNF-FG to a particular domain and its placement within the assigned local domain as a two-stage graph matching problem. To this purpose, we leverage graph attention networks in combination with hierarchical deep reinforcement learning. The learning process is additionally bootstrapped through self-supervised pre-training for both domain assignment and placement stages. The initial policies are further fine-tuned by the different agents along the learning process to address the evolving states of the domains. We show that our approach provides competitive real-time VNF-FG embedding results while achieving load balancing across the domains without compromising their privacy and autonomy in addition to satisfying QoS constraints. Our intelligent framework also paves the way for novel approaches that can benefit from the inherent graph structure of the problem.

The Software-Defined Networking (SDN) paradigm relies on decoupling the control and data planes, and logically centralizing SDN control to enable direct network programming via open interfaces. New abstractions are thus needed in a bid to rethink the traditional networking approach and create new opportunities for management and automation. We demonstrate the GOX controller, proposing a novel graph abstraction of the network topology in real time using the scalable Neo4j graph database. Our proof-of-concept was evaluated for a forwarding application designed for GOX. Compared to POX’s model, GOX shows better performance and scalability on synthetic topologies and real-world topologies from the Internet Topology Zoo.

In a Software-Defined Networking (SDN) architecture, Northbound, Southbound, and East-Westbound APIs are used to describe how interfaces operate between the three SDN planes, namely the data, control, and application planes. Apart from the standardization of the Southbound interface, for which OpenFlow has emerged as the widely-accepted standard, there is to date no open and vendor-neutral standard for the Northbound and East-West interfaces to provide the required interoperability between different SDN controller platform designs. This paper addresses the lack of a well-defined standard for the Northbound API that is used for the interaction between the applications and the SDN controllers, by proposing a GraphQL-based Northbound API design for the SDN controllers in the context of large-scale deployments. Our proof-of-concept methodology was validated and evaluated for an intent-based routing application that we designed on top of the ONOS controllers. When compared to ONOS’s native REST API, our Northbound API model proved efficient in optimizing different performance metrics (i.e the number of requests, the request execution time, and the throughput) on both synthetic and real-world network topologies (like Renater and China Telecom) that are emulated using Mininet.

This paper addresses the knowledge dissemination problem in distributed SDN control by proposing an adaptive and continuous consistency model for the distributed SDN controllers in large-scale deployments. We put forward a scalable and intelligent replication strategy following Quorum-replicated consistency: It uses the read and write Quorum parameters as adjustable control knobs for a fine-grained consistency level tuning. The main purpose is to find, at runtime, appropriate partial Quorum configurations that achieve, under changing network and workload conditions, balanced trade-offs between the application's continuous performance and consistency requirements. Our approach was implemented for a CDN-like application that we designed on top of the ONOS controllers. When compared to ONOS's static consistency model, our model proved efficient in minimizing the application's inter-controller overhead while satisfying the SLA-style application requirements.

Logically-centralized but physically-distributed SDN controllers are mainly used in large-scale SDN networks for scalability, performance and reliability reasons. These controllers host various applications that have different requirements in terms of performance, availability and consistency. Current SDN controller platform designs employ conventional strong consistency models so that the SDN applications running on top of the distributed controllers can benefit from strong consistency guarantees for network state updates. However, in large-scale deployments, ensuring strong consistency is usually achieved at the cost of generating performance overheads and limiting system availability. That makes weaker optimistic consistency models such as the eventual consistency model more attractive for SDN controller platform applications with high-availability and scalability requirements. In this paper, we argue that the use of the standard eventual consistency models, though a necessity for efficient scalability in modern SDN systems, provides no bounds on the state inconsistencies tolerated by the SDN applications. To remedy that, we propose an adaptive consistency model for the distributed ONOS controllers following the notion of continuous and compulsory (per-controller) eventual consistency, where network application states adapt their eventual consistency level dynamically at runtime based on the observed state inconsistencies under changing network conditions. When compared to the ONOS approach to static eventual consistency, our approach proved efficient in minimizing state synchronization overheads while taking into account application state consistency SLAs and without compromising the application requirements of high-availability, in the context of large-scale SDN networks.

The decoupling of control and data planes in Software-Defined Networking (SDN) brings benefits in terms of logically centralized control and application programming. But, the single point of management in physically centralized SDN architectures is a potential point of failure and a bottleneck that compromises network reliability and performance. Such centralized designs may also face scalability challenges especially in networks with a large number of hosts (e.g. IoT-like networks). To avoid such concerns, SDN control architectures are usually designed as physically distributed systems. This raises practical challenges about the best approach to decentralizing the control plane while maintaining the logically centralized network view. In particular, determining the number of controllers and locating them in the network is a hard task that should be addressed appropriately. This paper proposes two novel strategies that cover different aspects of the controller placement problem with respect to performance and reliability criteria. These strategies use two types of heuristics that are compared and assessed on large-scale topologies to provide operators with guidelines on how to find their optimal controller placement that meets their specific needs.