High-Fidelity Network Management for Federated AI-as-a-Service: Cross-Domain Orchestration

To support the emergence of AI-as-a-Service (AIaaS), communication service providers (CSPs) are on the verge of a radical transformation-from pure connectivity providers to AIaaS a managed network service (control-and-orchestration plane that exposes AI models). In this model, the CSP is responsible not only for transport/communications, but also for intent-to-model resolution and joint network-compute orchestration, i.e., reliable and timely end-to-end delivery. The resulting end-to-end AIaaS service thus becomes governed by communications impairments (delay, loss) and inference impairments (latency, error). A central open problem is an operational AIaaS control-and-orchestration framework that enforces high fidelity, particularly under multi-domain federation. This paper introduces an assurance-oriented AIaaS management plane based on Tail-Risk Envelopes (TREs): signed, composable per-domain descriptors that combine deterministic guardrails with stochastic rate-latency-impairment models. Using stochastic network calculus, we derive bounds on end-to-end delay violation probabilities across tandem domains and obtain an optimization-ready risk-budget decomposition. We show that tenant-level reservations prevent bursty traffic from inflating tail latency under TRE contracts. An auditing layer then uses runtime telemetry to estimate extreme-percentile performance, quantify uncertainty, and attribute tail-risk to each domain for accountability. Packet-level Monte-Carlo simulations demonstrate improved p99.9 compliance under overload via admission control and robust tenant isolation under correlated burstiness.

💡 Research Summary

The paper addresses the emerging need for communication service providers (CSPs) to evolve from pure connectivity operators into AI‑as‑a‑Service (AIaaS) orchestrators that jointly manage network transport and AI inference resources. In AIaaS, end‑to‑end service quality is a combination of communication impairments (delay, loss) and inference impairments (latency, model error). Service‑level objectives (SLOs) are typically expressed as extreme‑percentile latency guarantees (e.g., p99 or p99.9), which makes tail‑risk management essential, especially when the service spans multiple administrative domains that must keep internal scheduling policies confidential.

To fill the gap between existing AI‑native networking proposals and the need for enforceable, cross‑domain guarantees, the authors introduce Tail‑Risk Envelopes (TREs). A TRE is a signed, per‑domain contract consisting of deterministic rate‑latency guardrails (R, T) and stochastic impairment parameters (κ, η) that bound the moment‑generating function (MGF) of the residual impairment process. Formally, for domain d and a chosen reservation level, TRE_d(θ) = {R_d, T_d, κ_d, η_d} and the service satisfies
S_d(s,t) ≥ R_d·(t‑s)‑T_d + I_d(s,t) with E