6G Networks Enable Future Services Via Service Registration, Indexing, Discovery and Selection Architecture

The increasing complexity of future sixth-generation (6G) networks demands innovative approaches to service management, and a team led by Mohammad Farhoudi from University of Oulu, Masoud Shokrnezhad from ICTFICIAL Oy, and Tarik Taleb from Ruhr University Bochum, comprehensively surveys the critical elements of Service Registration, Indexing, Discovery, and Selection (SRIDS).

This research addresses a fundamental challenge in 6G networks, where exponentially growing use cases and dynamic traffic patterns require robust and adaptable service orchestration. By establishing a theoretical foundation for SRIDS and systematically analysing existing architectures, the team identifies key gaps hindering unified service management, and proposes a novel hybrid framework. Importantly, this framework incorporates Generative Artificial Intelligence (GenAI) to enhance scalability and agility, paving the way for a future where 6G networks can seamlessly support a diverse range of demanding applications. SDN, NFV and Network Programmability Trends This work presents a comprehensive overview of recent research in networking and distributed systems, encompassing areas like security, artificial intelligence, and blockchain technologies. The research focuses on foundational technologies such as Software-Defined Networking (SDN) and Network Function Virtualization (NFV), which enable flexible and programmable networks, and explores their application in large-scale network deployments. A key area of investigation is network slicing, leveraging SDN and NFV to create dedicated network segments tailored to specific applications and services. Researchers also focus on edge computing, pushing computational resources closer to network users to reduce latency and improve performance through optimised caching strategies and resource allocation. The study explores the integration of mobile networks with cloud computing, aiming to provide seamless and scalable services, and examines various consensus algorithms, including Paxos and DAG-based methods, to improve the efficiency and scalability of distributed systems and blockchain networks. Furthermore, the research investigates the application of artificial intelligence and machine learning for intelligent network optimisation, resource allocation, and anomaly detection, employing predictive modelling to forecast network traffic and potential issues. Reinforcement learning techniques are also explored for dynamic resource allocation and network control, with a growing emphasis on making AI-driven network decisions more transparent and understandable through Explainable AI. A major theme of the research is addressing the threat posed by quantum computers to current cryptographic algorithms, focusing on implementing and evaluating post-quantum cryptographic solutions. Researchers investigate the security of blockchain networks and explore techniques to enhance their resilience against attacks, while also developing privacy-preserving federated learning techniques to train machine learning models on decentralised data without compromising user privacy. Secure data sharing methods in distributed environments and advancements in network security, including anomaly detection and intrusion prevention, are also central to the work.

This research demonstrates a convergence of networking, AI, security, and blockchain technologies, with a recurring focus on scalability, efficiency, and security. The work is geared towards solving real-world problems in areas such as mobile communications, the Internet of Things, transportation, and cloud computing, requiring an interdisciplinary approach that combines expertise in networking, computer science, mathematics, and engineering. The authors, affiliated with universities in Iran and Finland, demonstrate a strong collaboration between these institutions, with Tarik Taleb playing a key leadership role in the research area. Researchers systematically reviewed current standardisation efforts and projected future design objectives, prioritising reliability, scalability, security, privacy, and trust as critical performance indicators. The study employed a rigorous taxonomy, classifying SRIDS mechanisms into centralised, distributed, decentralised, and hybrid architectures to facilitate comparative analysis of existing research.

The team evaluated each identified study against the established design objectives, allowing them to pinpoint conceptual and methodological gaps hindering unified SRIDS in 6G systems. Building upon these findings, scientists proposed a novel hybrid architectural framework, strategically combining centralised data management to ensure consistency and agility with distributed coordination to enhance scalability in emerging 6G use cases. The research highlights the necessity for SRIDS mechanisms to excel in three critical dimensions: performance, security, and adaptability, considering the stringent quality-of-service requirements, high dynamism of user mobility and distributed infrastructure, and the need for democratised access. To achieve this, the team emphasises continuous performance monitoring, enabling automatic re-registration upon service property changes, dynamic re-indexing with instance updates, and proactive re-selection to maintain optimal quality as system states evolve, ensuring a resilient and efficient service framework for future 6G networks. By establishing theoretical foundations and analysing existing literature, the team identified gaps in current SRIDS frameworks and proposed a novel taxonomy classifying architectures as centralised, distributed, decentralised, or hybrid. The core achievement lies in the development of a hybrid architectural framework that strategically combines centralised data management for consistency with distributed coordination to enhance scalability, addressing the demands of emerging 6G applications. The work systematically evaluates existing SRIDS mechanisms against key design objectives including reliability, scalability, and security, providing a clear understanding of current limitations and potential improvements. Recognising the dynamic nature of 6G service provisioning, the researchers highlight the need for adaptive machine learning techniques and predictive analytics to optimise performance and address network challenges. These findings contribute to a deeper understanding of SRIDS architectures and pave the way for the development of robust and scalable service discovery mechanisms in future 6G networks. 👉 More information 🗞 Service Registration, Indexing, Discovery & Selection; An Architectural Survey Toward a GenAI-Driven Future 🧠 ArXiv: https://arxiv.org/abs/2512.07638 Tags: