![[2602.15286] AI-Paging: Lease-Based Execution Anchoring for Network-Exposed AI-as-a-Service](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.15286] AI-Paging: Lease-Based Execution Anchoring for Network-Exposed AI-as-a-Service
Summary
The paper presents AI-Paging, a framework for optimizing AI-as-a-Service by enabling network providers to manage model selection and execution based on user intent and Quality of Service (QoS) constraints.
Why It Matters
As AI-as-a-Service becomes more prevalent, efficient management of AI model execution is critical. This framework addresses the challenges of intent resolution and service continuity, making it relevant for network providers and AI service developers in enhancing user experience and service reliability.
Key Takeaways
- AI-Paging allows network providers to match user intent with AI models dynamically.
- The framework ensures service continuity through lease-gated steering and make-before-break anchoring.
- Prototyping demonstrates compatibility with existing network architectures, enhancing deployment feasibility.
- AI-Paging addresses QoS constraints, improving the reliability of AI services.
- The proposed architecture can reduce transaction latency and improve service management under dynamic conditions.
Computer Science > Networking and Internet Architecture arXiv:2602.15286 (cs) [Submitted on 17 Feb 2026] Title:AI-Paging: Lease-Based Execution Anchoring for Network-Exposed AI-as-a-Service Authors:Merve Saimler, Mohaned Chraiti View a PDF of the paper titled AI-Paging: Lease-Based Execution Anchoring for Network-Exposed AI-as-a-Service, by Merve Saimler and Mohaned Chraiti View PDF HTML (experimental) 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 expiring admission lease (COMMIT) that authorizes user-plane steering to a selected AI execution anchor (AEXF) under a QoS binding. AI-Paging enforces two invariants: (i) lease-gated stee...
