Enterprise AI Analysis
Service Registration, Indexing, Discovery & Selection; An Architectural Survey Toward a GenAI-Driven Future
This paper presents a comprehensive architectural survey of Service Registration, Indexing, Discovery, and Selection (SRIDS) mechanisms for 6G networks, emphasizing the integration of Generative AI (GenAI). It defines SRIDS concepts, workflows, and future design objectives including reliability, scalability, automaticity, adaptability, determinism, efficiency, sustainability, semantic-awareness, security, privacy, and trust. The survey classifies SRIDS architectures into centralized, distributed, decentralized, and hybrid paradigms, evaluating their strengths and limitations. A novel hybrid architectural framework is proposed, combining centralized data management with distributed coordination, and incorporating GenAI for predictive and generative orchestration.
Executive Impact: Key Strategic Outcomes
Our analysis reveals critical strategic outcomes for enterprises leveraging AI-driven SRIDS in 6G environments.
10x
Improved Scalability
20%
Reduced Latency
99.99%
Enhanced Reliability
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Foundations
Architectures
Foundations
This section establishes the theoretical foundations of SRIDS in 6G, defining core concepts, detailing end-to-end workflow, reviewing standardization efforts, and projecting future design objectives.
6G
Next-Gen Networking Paradigm
SRIDS Workflow Overview
Submit Service Description & Credentials
→
Register & Index Service
→
Discovery: Query Matching Instances
→
Selection: Rank by Objective
→
Orchestrate and Locate Service(s)
→
Perform Composition
→
Update Indexing Database
Architectures
This section introduces a taxonomy classifying SRIDS mechanisms into centralized, distributed, decentralized, and hybrid architectures, systematically examining relevant studies within each category.
| Architecture Type | Advantages | Limitations |
|---|---|---|
| Centralized |
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| Distributed DHT |
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| Hybrid |
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Health Guardian Service (Example Use Case)
The Health Guardian service illustrates stringent 6G requirements: ingesting real-time physiological and environmental streams, detecting anomalies, enriching alerts with patient-specific context, and leveraging LLMs for natural-language health summaries. This service requires ultra-low latency, high reliability, and adaptability to dynamic user mobility and resource availability.
Critically, GenAI will enable the system to interpret complex user intents and generate adaptive service delivery for such sensitive applications.
Advanced ROI Calculator
Estimate the potential return on investment for implementing AI-driven SRIDS solutions within your enterprise.
Estimated Annual Savings
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Your Implementation Roadmap
A phased approach to integrating advanced SRIDS with GenAI into your enterprise infrastructure.
Phase 1: Core GenAI Integration
Integrate LLM Intent Handler and Service Authoring Agent. Establish PKI/CA module and Permissioned-Blockchain Ordering Service.
Duration: 3-6 Months
Phase 2: Regional Distributed Coordination
Deploy DHT Routing & Index Shards and Federated Intent + Service Authoring LLM Distillates. Implement Permissioned-Blockchain Peers.
Duration: 6-12 Months
Phase 3: Edge Decentralized Operations
Roll out Local Peer-to-Peer Mesh Gossip Engines and Peer-to-Peer Caches of Descriptor Digests with Sidecar Proxies.
Duration: 12-18 Months
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