Executive Outcomes
100×
Faster change validation via what‑if
↓ CFR
Lower change failure rate with twin gates
SLO
Track latency/packet‑loss budgets by change
Audit
Evidence packs for every decision
Constraints: Snapshot cadence, coverage of L2/L3/L4 data, and quality of intent policies determine ceiling of accuracy.
1) Digital Twin Theory — from Model to Intelligence
Graph ModelCausalityCounterfactualsWhat‑If Simulation
- Graph substrate: Devices/links as nodes/edges; attributes carry health, policy, traffic.
- Counterfactuals: “If link X fails / timer Y changes, then …” → intervene on the graph and compare pre/post SLOs.
- Learning on graphs: Use topology features (degree, betweenness, community) as priors; combine with time‑series from snapshots.
- Verification loop: Simulate → verify against intent rules → accept/reject → learn.
IP Fabric mapping: Snapshots provide time‑indexed states; path & intent engines validate outcomes; twin is the safe sandbox for interventions.
2) MLOps Theory — productionizing network intelligence
- Experimentation → Serving: Track data versions, features, and runs; serve models behind SLOs and canary gates.
- Monitoring: Data drift, concept drift, latency, error budgets; automatic rollback.
- Governance: Model registry, approvals, reproducibility, and audit trails per tenant.
Decision rubric: on‑prem vs managed inference
IF data_residency_strict OR low_latency_edges → on‑prem GPU/CPU with quantized models
ELSE → managed inference with private networking & encryption
IF per‑tenant isolation required → one‑model‑per‑tenant or feature‑store partitioning
3) Spec: Digital Twin What‑If API (No raw code)
Endpoint: POST /twin/whatif
Request:
{
"snapshot_id": "SNAP_2025_09_12_10_00",
"scenario": {"type":"modify_config","targets":["core-r1"],"changes":[{"feature":"bgp","graceful_restart":true}]},
"slo": {"latency_ms": 120, "loss_pct": 0.1},
"policies": ["intent:edge-segmentation","intent:routing-redundancy"]
}
Response:
{
"pre_state": {"latency_p50": 85, "loss": 0.03},
"post_state": {"latency_p50": 90, "loss": 0.03},
"intent_results": [{"policy":"routing-redundancy","pass":true}],
"blast_radius": {"devices": 6, "paths": 12},
"risk_score": 0.22,
"decision": "APPROVE",
"evidence_pack_url": "/reports/whatif/abcd1234.html"
}
4) Pattern Library — Twin Intelligence
Pattern A — Topology‑Aware Risk
Features: degree, edge_betweenness, path_entropy, ecmp_count, historical_flaps
Model Options: rules + calibrated classifier (GBM) + LLM rationale
Output: risk_score ∈ [0,1], hotspots, recommended maintenance window
Pattern B — Capacity & Congestion Forecast
Input: time‑series per interface (util%, queues, drops), seasonalities
Method: STL or Prophet + residual anomaly detector
Action: raise ticket if projected breach of SLO in T±7 days; attach path impacts
Pattern C — Intent‑Safe Change Plan
Input: change_request (feature, scope, window)
Process: simulate → check intents → compute blast_radius → compile approval packet
Output: plan.md + rollback.yaml + test_matrix.json
5) MLOps Contracts — from training to rollback
Contract A — Training Job
POST /ml/train
Body: { "dataset_uri":"s3://ipf/snapshots/**", "featureset":"topology_v3", "objective":"risk", "eval":["auc","f1","calibration"] }
Returns: { "run_id":"RUN_789", "metrics":{"auc":0.92}, "model_uri":"registry://risk:v17" }
Contract B — Canary Deploy
POST /ml/deploy
Body: { "model":"registry://risk:v17", "strategy":"canary", "weight":10, "rollback_thresholds":{"accuracy":0.95,"latency_ms":300} }
Returns: { "deployment_id":"DEP_112", "status":"CANARY", "grafana":"https://grafana/.../DEP_112" }
Contract C — Drift & Rollback
POST /ml/monitor
Body: { "deployment_id":"DEP_112", "drift_score":0.12, "latency_ms":280, "accuracy":0.96 }
If drift_score > 0.3 OR accuracy < 0.95 → POST /ml/rollback { "deployment_id":"DEP_112" }
6) Practical Playbooks (No code)
Playbook A — “Add WAN Link” Change
- What‑if: inject link, recompute ECMP and failover paths.
- Intent checks: redundancy, segmentation, max_latency.
- Decision packet: risk, blast radius, test matrix, rollback plan.
Playbook B — “QoS Policy Tuning”
- Simulate new class‑maps; predict queue drops.
- Compare pre/post SLOs; verify business‑critical apps.
- Gate rollout by golden signals with auto‑rollback.
Playbook C — “Redundancy Audit”
- Traverse topology; flag single points of failure.
- Recommend incremental fixes with twin validation.
- Create an executive heatmap report.
Week 2 Deliverables
- What‑If API spec + evidence pack template
- Risk/Capacity/Intent patterns (ready‑to‑implement)
- MLOps contracts (train/deploy/monitor/rollback)
- 3 executive playbooks and KPI definitions