Intent-Driven Framework for Autonomous Network Optimization

• The paper introduces a framework that formalizes network intents as KPI vectors and quantifies intent drift to drive corrective actions.
• It employs continuous feedback from real-time telemetry and LLM-based policy generation to automate network optimization.
• Experimental results show notable speed-ups and improved compliance compared to manual and static policy approaches.

An intent-driven framework for autonomous network optimization defines, assures, and fulfills high-level network objectives through the continuous and automated alignment of network state with user, operator, or application intents. This paradigm leverages formal specification of intents as KPI targets, real-time monitoring, intent drift quantification, AI-driven policy generation (notably with LLMs), and feedback control loops. The objective is to realize self-optimizing, SLA-compliant, and adaptive networks that minimize manual intervention while ensuring robust performance in dynamic environments (Dzeparoska et al., 1 Feb 2024).

1. Formalization of Intent and Assurance Concepts

At the foundation, a high-level intent $I$ is represented as a vector of Key Performance Indicators (KPIs):

$$\overrightarrow{K_I} = (k_1 : v_1, \; k_2 : v_2, \; \ldots, \; k_m : v_m)$$

where each $(k_i : v_i)$ encodes a named metric (e.g., availability, latency) and its desired value. The intent is realized by driving the network from the observed operational state $S_\mathrm{op}$ (as inferred from telemetry) to the target state $S_\mathrm{tgt}$ induced by $I$:

$$S_\mathrm{tgt} = f_\mathrm{fulfill}(I), \qquad S_\mathrm{op} = MAP(\mathrm{telemetry})$$

The central metric, intent drift, quantifies the misalignment between $S_\mathrm{op}$ and $S_\mathrm{tgt}$:

$$\Delta\overrightarrow{K} = \overrightarrow{K_O} - \overrightarrow{K_T}, \qquad D_E(S_\mathrm{op}, S_\mathrm{tgt}) = \|\Delta\overrightarrow{K}\|_2, \qquad D_S(S_\mathrm{op}, S_\mathrm{tgt}) = \sum_i \delta_i^2$$

where $\delta_i = k_i^o - k_i^t$. The drift gradient

$\nabla E = (2\delta_1, 2\delta_2, ..., 2\delta_n)$

provides actionable directionality for corrective policy generation (Dzeparoska et al., 1 Feb 2024).

Intent assurance is defined as a closed-loop map:

$A: (I, S_\mathrm{op}) \mapsto P_\mathrm{corr}$

where applying $P_\mathrm{corr}$ causes $D(S_\mathrm{op}, S_\mathrm{tgt}) \to 0$.

2. Closed-Loop Assurance Pipeline and Algorithmic Structure

The assurance process operates as a continuous feedback loop with the following stages:

1. Monitoring: Acquisition of live network telemetry and quantization to the current KPI vector.
2. Verification: Computation of intent drift; drift metrics are compared against a predefined threshold $\epsilon$.
3. Policy Generation: On observing unacceptable drift, a policy context is built (including intent targets, observed KPIs, drift, drift gradient, and correction history). This context is supplied to an LLM-based assurance model, which generates candidate corrective policies.
4. Enforcement: Policies are translated to actionable API calls, executed, and their effects logged for potential downstream adjustments.

Key subroutines include state mapping (quantization, policy function evaluation), drift metric computation, gradient calculation, and invoking LLMs for policy synthesis. Pseudocode reflecting this pipeline is provided in the primary source and reflects a tightly coupled observe-think-act-modulate cycle (Dzeparoska et al., 1 Feb 2024).

3. LLM-Based Corrective Policy Generation

LLMs are leveraged in two principal ways:

• Few-shot In-Context Prompting: The LLM (e.g., GPT) is primed with annotated intent/KPI contexts and step-by-step assurance reasoning. Prompts clarify which actions (from a controlled grammar: create, delete, scale, restart, route) are valid and emphasize convergence guarantees:
  • No repeated action on an object more than $N$ times in a window.
  • Only propose corrective actions that are estimated (using drift gradients) to produce $$\|\nabla E_\mathrm{new}\| < \|\nabla E_\mathrm{old}\|$$.
• Post-Policy Validation: Each generated corrective policy is type-checked, mapped to live API schemas, and—when applicable—simulated in a digital twin to verify that applying the policy indeed reduces intent drift. Failed or unsafe policies are rejected or rolled back in the enforcement phase.

Few-shot, template-driven prompting is chosen both for flexibility and for safety; empirical results show reliable assurance performance, though coverage limitations arise when prompted with previously unseen intent structures (Dzeparoska et al., 1 Feb 2024).

4. Experimental Performance and Benchmarks

Empirical outcomes from deployments show robust performance and overhead characteristics:

Metric	Automated LLM (mean)	Manual Fulfillment	Static Policy Engine
Fulfillment convergence time	393 s	~700 s	-
Assurance convergence time	89 s	~3–5 min	-
Compliance rate	100%	-	-
Rollback frequency	0%	-	-

LLM policy generation incurs ~25 s/invocation (constant across scale), while device API actions dominate end-to-end latencies. Linear scalability is observed with respect to devices/policies due to per-device action distribution. Memory overhead per LLM prompt is ~2 KB, with effective support for 10–20 concurrent in-context examples. No rollbacks were required in the evaluation, and all policies restored the targeted KPI (e.g., health) to compliance (Dzeparoska et al., 1 Feb 2024).

Baseline comparisons indicate a 1.8× speed-up over traditional manual scripting for initial fulfillment and a ~2× improvement in assurance reaction time compared to human operator-driven recovery.

5. Generalization, Limitations, and Enhancement Pathways

Several limitations and areas for future work are identified:

• Few-shot Generalization and Coverage: Reliance solely on in-context demonstrations leads to coverage gaps for new intent types or rare fault conditions. The inclusion of a vector database, feeding retrieval-augmented prompts, is proposed to broaden generalization.
• Optimization Objectives: The present framework corrects drift on a per-KPI basis, lacking full multi-objective optimization. Extension is feasible by computing a joint multi-objective gradient and prompting the LLM to select actions according to a constrained optimization subproblem.
• Safety and Validation: Systematic simulation in a digital twin is necessary to validate the safety and efficacy of all candidate corrective policies before deployment, especially as network models and attack surfaces grow in complexity.
• Cross-Domain Integration: Support for operational technology (OT) metrics, in addition to traditional IT indicators, requires richer, formally defined policy functions and possibly heterogeneous LLM capabilities.

The architecture is extensible: additional modalities (multi-objective optimization, digital twin interfacing, retrieval-augmented LLMs) can be supported within the existing formal and procedural framework, retaining closed-loop autonomy (Dzeparoska et al., 1 Feb 2024).

6. Context and Connections to the Broader Literature

The described framework represents a concrete instantiation of the intent-assurance pillar in intent-based networking. Unlike earlier position-only or rule-based systems, this architecture formalizes not only the definition and detection of drift but also the automated closure of the control loop using LLM-driven policy generation and feedback. Experimental performance demonstrates improvement factors over both manual and rule-based baselines, and the software architecture is well positioned for integration with contemporary telemetry, API, and digital twin systems.

The approach conceptually aligns with recent literature on the fusion of AI-driven closed-loop management and full-lifecycle assurance in intent-driven networks, as explored in frameworks such as SAFLA (Kou et al., 18 Apr 2024), as well as broader intent translation and optimization strategies integrating diverse ML/AI methods.

7. Summary Table: Key Components of the Intent-Driven Assurance Framework

Component	Description	Core Methods
Intent Specification	High-level KPI vector for business/service objectives	Formal vector definition
Monitoring	Real-time KPI capture and state mapping	Telemetry + quantization
Drift Detection	Vector drift and metric calculation	Euclidean/squared-error
Policy Generation	Corrective action proposal using context-aware LLM	Few-shot LLM prompting
Enforcement	Policy API invocation; effect logging and feedback	API mapping + simulation
Assurance Loop	Continuous closed-loop control; react, verify, adapt	State-feedback control

This integrated approach achieves a fully automated, intent-aligned, autonomic network capable of dynamic self-optimization, robust assurance, and minimal human-in-the-loop intervention. By formalizing intent, quantifying drift, and leveraging LLM-powered policy generation within a scalable, closed-loop architecture, the framework delivers practical and empirically validated autonomous network optimization (Dzeparoska et al., 1 Feb 2024).