AI-Paging: Lease-Based Execution Anchoring for Network-Exposed AI-as-a-Service
Mohaned Chraiti, Merve Saimler

TL;DR
AI-Paging introduces a lease-based control mechanism for dynamic, policy-compliant AI service execution placement in 6G networks, ensuring continuity and reliability amid network changes.
Contribution
It proposes a novel lease-based AI paging architecture that integrates with existing network control mechanisms for efficient AI service placement.
Findings
Prototype implementation demonstrates compatibility with 3GPP architectures.
Evaluation shows low transaction latency and reliable service continuity.
Lease enforcement maintains service integrity during mobility and failures.
Abstract
With AI-as-a-Service (AIaaS) now deployed across multiple providers and model tiers, selecting the appropriate model instance at run time is increasingly outside the end user's knowledge and operational control. Accordingly, the 6G service providers are envisioned to play a crucial role in exposing AIaaS in a setting where users submit only an intent while the network helps in the intent-to-model matching (resolution) and execution placement under policy, trust, and Quality of Service (QoS) constraints. The network role becomes to discover candidate execution endpoints and selects a suitable model/anchor under policy and QoS constraints in a process referred here to as AI-paging (by analogy to cellular call paging). In the proposed architecture, AI-paging is a control-plane transaction that resolves an intent into an AI service identity (AISI), a scoped session token (AIST), and an…
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Taxonomy
TopicsAge of Information Optimization · Human Mobility and Location-Based Analysis · IPv6, Mobility, Handover, Networks, Security
