This document shows a practical reference architecture to add AI-powered Adaptive Deployment Strategies (blue/green, canary, rolling) to your existing Jenkins pipeline spanning ARM, Linux, and Windows builds. It also details the monitoring and auto-rollback loop, artifacts/versioning, and tooling choices.
- Code & Artifacts: Git → Jenkins (multi-OS parallel pipelines) → Artifactory
- Orchestration: Kubernetes (Linux/ARM services), Windows (IIS/Windows Service + ARR/LB), ARM/edge cohorts (OTA manager)
- Progressive Delivery: Argo Rollouts or Flagger (K8s), ARR pool weights (Windows), cohort percentages (ARM)
- AI Layer:
- Risk Scoring (pre-deploy): choose strategy & pace
- Canary/Blue-Green Analysis (during deploy): promote/slow/rollback
- Observability: OpenTelemetry → Prometheus (metrics) + Loki/ELK (logs) + Tempo/Jaeger (traces) + Grafana (dashboards)
- Policy/Safety: OPA gates, SLO error budgets, change windows
- Release Evidence: Build-Info + deployment decisions saved to Artifactory (or DB)
┌──────────────────────────────────────────────────────┐
│ Git │
└───────────────▲──────────────────────────────────────┘
│
Jenkins (Multibranch)
│
┌─────────────────────────┼─────────────────────────┐
│ │ │
ARM Pipeline Linux Pipeline Windows Pipeline
(sync→build→ (sync→build→package→ (sync→build→package→
package→publish) publish) publish)
│ │ │
└───────────────┬─────────┴─────────┬───────────────┘
│ │
Integration Bundling / Release Candidate
│
┌────────▼────────┐
│ AI: Risk Score │ (uses commit diff, tests, deps,
│ + Strategy │ service criticality, traffic)
└────────┬────────┘
│ decides: canary | blue/green | rolling
┌────────────────────┼──────────────────────────────────────────────────┐
│ │ │
│ Kubernetes (Linux/ARM) Windows (IIS)
│ ┌───────────────────────────┐ ┌─────────────────────┐
│ │ Argo Rollouts / Flagger │ │ ARR/LB + Slots │
│ │ Helm/Kustomize Deploy │ │ (Blue/Green/Canary) │
│ └─────────────┬─────────────┘ └────────────┬────────┘
│ │ Health Checks
│ Canary/Blue/ (perf/errors/KPIs)
│ Rolling flows │
└───────────────┬──────┴──────────────────────────────────────────────┘
│
AI Canary/Green Analyzer (metrics + logs + traces vs baseline)
│ promote / slow / rollback
▼
Promote to 100% → Tag latest in Artifactory (build-info)
│
Notify/Jira/Slack + Evidence Links (Grafana, logs)
- Parallel builds (ARM/Linux/Windows) → publish artifacts to Artifactory.
- Integration Bundle (compose cross-OS deliverables if needed).
- AI: Risk Score & Strategy Selection → emits
DEPLOY_STRATEGY+ pace. - Deploy per target
- K8s: Helm + Argo Rollouts/Flagger (canary, B/G, rolling).
- Windows: IIS slots (B/G), ARR pool weights (canary), rolling batches.
- ARM/Edge: OTA cohorts 1% → 5% → 20% → 100%.
- AI Analyzer monitors SLOs/KPIs vs last-stable baseline → promote/slow/rollback.
- Publish Evidence: risk score, strategy, health windows, SLOs, result → Artifactory Build-Info (+ dashboard links).
- CI/CD: Jenkins (Shared Library for AI stages)
- Packaging: Docker/Helm for k8s; MSI/NSSM/IIS for Windows; OTA images for ARM
- Registry/Artifacts: JFrog Artifactory
- Progressive Delivery: Argo Rollouts or Flagger (k8s); IIS slots/ARR (Windows); Mender/Balena/Custom (ARM)
- Observability:
- Metrics: Prometheus (+ kube-state-metrics, cAdvisor)
- Logs: Loki or ELK
- Traces: OpenTelemetry SDKs → Tempo/Jaeger
- Dashboards/Alerts: Grafana
- AIOps/AI: Python microservices/scripts (risk scoring, canary analysis) packaged as containers
- Policy: OPA/Gatekeeper or Jenkins quality gates
- Comms: Slack/MS Teams; Jira for incidents
- RED/USE metrics: request rate, error rate (HTTP 5xx), p95/p99 latency; CPU, memory, saturation
- Business KPIs: success ratio (e.g., payment success), downstream error ratio
- Windows-specific: IIS AppPool health, Event Log error signatures
- ARM-specific: device error beacons, OTA success/failure
- Baseline = last stable release’s metrics on the same time-of-day/load or synthetic load.
- Compare candidate vs baseline using rolling windows (e.g., 2 min windows for 10–20 minutes).
- Statistical checks: EWMA drift, z-score, non-parametric tests (Mann–Whitney) for latency distributions.
- Promote if all SLOs healthy for
Nconsecutive windows. - Slow if marginal (near SLO), extend observation & reduce pace.
- Rollback on sustained SLO breach, sharp error spike, or crash loops.
- Grafana Alerting/Alertmanager routes to Slack/Jira with runbook links.
- Store analyzer output JSON + plots in artifact repo (tied to build number).
Inputs: lines changed, files, dependency bumps, test coverage Δ, historical failure rate of module, criticality tag, current QPS/load, change window (peak/off-peak).
Output: { risk_score: 0–100, strategy: canary|bluegreen|rolling, pace }
Phase 1 (heuristic): weighted sum with clamps. Phase 2 (ML): train logistic/XGBoost using your history (label: success/rollback).
Inputs: Prometheus queries for candidate & baseline; logs anomaly score; trace error spans.
Logic: compute SLO status + drift score → action promote|slow|rollback.
apiVersion: argoproj.io/v1alpha1
kind: Rollout
metadata:
name: svc
spec:
strategy:
canary:
steps:
- setWeight: 10
- pause: {duration: 2m}
- analysis: { templates: [{ templateName: kpi-check }]}
- setWeight: 25
- pause: {duration: 2m}
- setWeight: 50
- pause: {duration: 3m}
- setWeight: 100apiVersion: argoproj.io/v1alpha1
kind: AnalysisTemplate
metadata:
name: kpi-check
spec:
metrics:
- name: error-rate
provider:
prometheus:
address: http://prometheus.monitoring:9090
query: sum(rate(http_requests_total{job="svc",status=~"5.."}[1m])) /
sum(rate(http_requests_total{job="svc"}[1m]))
failureCondition: result > 0.01
- name: p95-latency
provider:
prometheus:
address: http://prometheus.monitoring:9090
query: histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket{job="svc"}[1m])) by (le))
failureCondition: result > 0.3- Maintain
svc-blueandsvc-greenDeployments. - Flip Service/Ingress to point to
greenafter analysis passes.
- Blue/Green: Two IIS sites (Blue = live, Green = new). Swap bindings after health checks.
- Canary: Use ARR/LB to route 10%/25%/50% traffic to the Green pool; advance on healthy windows.
- Rolling: Update node pool in batches; verify Event Logs + synthetic checks.
Telemetry: WinRM/Telegraf → Prometheus; ship Event Logs to Loki/ELK; expose custom app metrics endpoint.
- Tag devices into cohorts (1% → 5% → 20% → 100%).
- OTA manager (e.g., Mender/Balena or custom) pushes candidate to next cohort on AI analyzer approval.
- Health signals: heartbeat, error codes, crash rate, customer-visible KPIs.
stage('AI: Decide Strategy') {
steps {
sh 'python3 ci/ai_decide_strategy.py --inputs ci/changes.json > strategy.json'
script {
def s = readJSON file: 'strategy.json'
env.DEPLOY_STRATEGY = s.strategy
env.RISK_SCORE = s.risk
env.PACE = s.pace
}
}
}
stage('Deploy (by strategy)') {
steps {
script {
if (env.DEPLOY_STRATEGY == 'canary') {
sh 'helm upgrade --install svc ./charts/svc --set canary.enabled=true --wait'
} else if (env.DEPLOY_STRATEGY == 'bluegreen') {
sh './ci/flip_blue_green.sh'
} else {
sh 'helm upgrade --install svc ./charts/svc --set rolling=true --wait'
}
}
}
}
stage('AI: Analyze & Decide') {
steps {
sh 'python3 ci/analyze_metrics.py --slo slo.yaml > verdict.json'
script {
def v = readJSON file: 'verdict.json'
if (v.action == 'promote') { sh './ci/promote_rollout.sh' }
else if (v.action == 'slow') { sh './ci/slowdown.sh' }
else { sh './ci/rollback.sh'; error('Rollback triggered') }
}
}
}
stage('Publish Evidence') {
steps {
sh """
jf rt build-add-props my-build ${env.BUILD_NUMBER} \
risk=${env.RISK_SCORE};strategy=${env.DEPLOY_STRATEGY};result=${currentBuild.currentResult}
jf rt build-publish my-build ${env.BUILD_NUMBER}
jf rt set-props 'repo/path/*${env.BUILD_TAG}*' latest=true
"""
}
}# Error rate (>1% for 3m)
- alert: HighErrorRate
expr: sum(rate(http_requests_total{job="svc",status=~"5.."}[1m])) /
sum(rate(http_requests_total{job="svc"}[1m])) > 0.01
for: 3m
labels: { severity: critical }
annotations:
summary: "svc error rate > 1%"
# p95 latency (>300ms for 5m)
- alert: HighLatencyP95
expr: histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket{job="svc"}[1m])) by (le)) > 0.3
for: 5m
labels: { severity: warning }
annotations:
summary: "svc p95 latency regression"- OPA: block deploys during peak windows or when error budget exhausted.
- Change windows: calendar-based Jenkins gate.
- Manual approval: optional for high risk (>70) before Blue/Green flip.
- Service Health: RED/USE metrics + error budget burn-down
- Release Compare: baseline vs candidate (dual-axis)
- Windows/IIS: app pool restarts, HTTP errors, queue length
- ARM Fleet: cohort progress, device failures, OTA success rate
- Check Risk Score & chosen strategy.
- Watch Release Compare dashboard during canary windows.
- If alerts fire → Jenkins will roll back; capture ticket with evidence.
- On success → promote to 100% and mark latest in Artifactory.
- Phase 1: Heuristic risk + Prometheus SLO checks (no ML training needed).
- Phase 2: Train failure-probability model; add ARR/Windows + ARM cohorts.
- Phase 3: Add unsupervised drift detection (Isolation Forest), OPA budgets, and feature-flag canaries at user level.
ci/ai_decide_strategy.py(heuristic now, ML-ready signature)ci/analyze_metrics.py(PromQL queries + decision JSON)- Helm/Argo Rollouts manifests + AnalysisTemplate
- Jenkins shared library steps for decide / deploy / analyze / promote / rollback
- Grafana dashboards: Release Compare, SLOs, ARM Cohorts, Windows/IIS
- Alerting rules + runbook links