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

TL;DR

This paper proposes a novel AIaaS management framework using Tail-Risk Envelopes and stochastic calculus to ensure high-fidelity, end-to-end service quality across multiple domains, addressing latency and impairment challenges.

Contribution

It introduces an assurance-oriented control plane with TREs, stochastic delay bounds, and runtime telemetry for accountability in multi-domain AIaaS networks.

Findings

Improved p99.9 delay compliance under overload conditions.

Effective tenant isolation during correlated burstiness.

Enhanced end-to-end performance guarantees using TREs.

Abstract

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.