Self-Driving Kubernetes via Neuro-Symbolic Controllers

Self-Driving Kubernetes via Neuro-Symbolic Controllers

Voruganti Kiran Kumar
Senior DevOps Engineer

Abstract— We present a neuro-symbolic controller for Kubernetes that closes the loop from detection to safe remediation. Neural components transform high-volume telemetry (logs, events, metrics, traces) into calibrated risk signals via representation learning and anomaly detection. Symbolic components encode safety and governance policies—expressed in Rego and CEL—and reason over admissible actions (e.g., adjust resources, cordon/quarantine nodes, shift traffic, or roll back workloads). Actions are executed through progressive exposure (canarying) and are explainable at decision time by fusing feature attributions from the neural models with rule-level justifications from the policy engine. We detail the architecture, action semantics, and an evaluation protocol emphasizing mean-time-to-recover (MTTR), incident rate, false remediations, and operator-rated explanation utility. A Kubernetes-native blueprint integrates OpenTelemetry, Open Policy Agent (OPA), ValidatingAdmissionPolicy, and GitOps controllers.

Index Terms— Kubernetes, anomaly detection, OPA, CEL, autoscaling, self-healing, admission control, explainability, GitOps.

I. Introduction

Kubernetes automates scheduling and orchestration, yet operational decisions—diagnosing anomalies, adjusting resources, isolating unhealthy nodes, or rolling back releases—still depend heavily on human interpretation of noisy telemetry. Conventional self-healing (e.g., restarts, basic autoscaling) lacks situational awareness and policy context, while purely statistical anomaly detectors lack action semantics and guardrails. The core challenge is to perceive early signals of emerging problems and act safely under explicit policies.

We propose a self-driving controller that combines (i) neural perception for robust anomaly and drift detection from heterogeneous telemetry with (ii) symbolic reasoning over policy-as-code to select and stage safe remediations. The controller explains its choices with a dual narrative: which signals mattered (model attributions) and which rules allowed or forbade actions (policy proofs). The result is a closed loop that reduces MTTR and incident volume while preserving governance and operator trust.

Contributions.

1. A neuro-symbolic architecture that turns telemetry into governed actions with canarying and rollbacks.

2. An action lattice with preconditions, effects, and safety contexts, compiled into Rego/CEL policies.

3. An explanation layer combining feature attributions and rule proofs.

4. A Kubernetes-native implementation blueprint and an evaluation protocol focused on reliability, safety, and human interpretability.

II. Background and Related Work

Log-based anomaly detection. Sequence-modeling approaches such as DeepLog learn normal log patterns and flag deviations, providing a foundation for unsupervised detection in distributed systems.

Policy-as-code. OPA/Rego provides a declarative policy engine widely adopted for cluster governance and admission control. Kubernetes ValidatingAdmissionPolicy exposes an in-process CEL path for low-latency, declarative validation at API admission, complementing OPA webhooks.

SRE and progressive delivery. SRE practice emphasizes objective guardrails, error-budget discipline, and rapid rollback, principles we embed in the action staging layer.

III. Problem Formulation

Let T\mathcal{T}T denote multivariate telemetry streams (logs, metrics, events, traces). A neural encoder N\mathcal{N}N maps windows of T\mathcal{T}T to risk scores r∈[0,1]r \in [0,1]r∈[0,1] and explanatory features zzz (e.g., unusual log subsequences, metric residuals). Let a symbolic policy knowledge base P\mathcal{P}P (Rego/CEL) evaluate state facts sss (cluster inventory, health, RBAC, environment labels) and r,zr,zr,z to select admissible actions AAA with preconditions and effects. An execution controller applies a∈Aa\in Aa∈A under progressive exposure and monitors service-level indicators (SLIs) for rollback.

Goals.
(i) Maximize reliability (reduce incident frequency, MTTR) subject to safety policies;
(ii) Minimize false remediations (harmful or unnecessary actions);
(iii) Maintain explainability and auditability for each decision.

IV. Architecture

A. Neural Perception Layer

1. Unified telemetry bus. Normalize logs, metrics, and traces (e.g., OpenTelemetry).

2. Parsing & embeddings. Template logs into sequences; learn token embeddings and sequence models for next-event prediction; fit residual models for metrics.

3. Risk head. Produce calibrated anomaly scores rrr, signal type tags (resource pressure, network anomalies, crash loops), and confidence intervals.

4. Attribution. Compute local attributions (e.g., gradient/perturbation or attention-based saliency) over tokens and metrics to ground explanations.

B. Symbolic Governance Layer

1. Action lattice. Define actions with preconditions, effects, and scopes:

• Tune resources (HPA/VPA setpoints, request/limit adjustments).

• Traffic shaping (reduce concurrency, canary to smaller cohort, regional failover).

• Workload control (pause rollout, rollback to last good revision).

• Node hygiene (cordon/drain suspected nodes; quarantine pool).

• Network posture (tighten NetworkPolicy for noisy pods).

2. Policies (Rego/CEL). Encode admissibility: which actions are allowed in which namespaces, times, or risk contexts; tie to labels (e.g., tier=pci forbids certain automated steps; only advisory changes permitted out of hours).

3. Conflict resolution. Explicit priorities and break-glass clauses with short-lived approvals and mandatory post-mortems.

C. Staged Execution and Feedback

1. Canarying. Apply actions to minimal safe scope (e.g., one replica set, a subset of nodes).

2. Guardrails. Promote only if SLIs remain within envelopes and risk rrr decreases; otherwise rollback and escalate.

3. Learning. Log (context, action, outcome, explanation) for offline policy improvement.

D. Explanation & Audit

• Why this action? Top anomalous signals and their attributions.

• Why allowed? Policy rule IDs and entailments showing admissibility.

• Why safe? Canary outcomes and SLI deltas.
  Artifacts are signed and attached to the change event for auditor replay.

V. Algorithms

A. Risk Signal Construction

• Log sequences: train a next-event predictor; anomaly rlogr_{\text{log}}rlog​ is the negative log-likelihood of observed sequences.

• Metrics residuals: fit seasonal baselines; anomaly rmetr_{\text{met}}rmet​ from residual magnitude with uncertainty.

• Fusion: r=σ(αrlog+βrmet+γ⋅event flags)r = \sigma(\alpha r_{\text{log}} + \beta r_{\text{met}} + \gamma \cdot \text{event flags})r=σ(αrlog​+βrmet​+γ⋅event flags) with calibration; output confidence.

B. Action Selection

Policies evaluate contexts c=(r,confidence,s)c=(r,\text{confidence},s)c=(r,confidence,s). A preference ordering selects the least disruptive action that is admissible and predicted to reduce rrr. Example Rego sketch (informal):

allow_action["rollback"] {

input.namespace.tier == "prod"

input.release.age < "2h"

input.risk.score >= 0.7

input.sli.error_rate_delta > 0.2

}

allow_action["vpa_tune"] {

input.namespace.tier in {"prod","staging"}

input.risk.type == "resource_pressure"

not input.namespace.safety_lock

}

C. Safety Staging

Given action aaa, define exposure plan: scope size, dwell time, promotion thresholds, and abort triggers. Plans follow SRE discipline and depend on environment tier.

D. Explanation Fusion

• Neural narrative: salient tokens/events contributing to rrr.

• Symbolic narrative: rules that permitted/blocked actions, with references to inputs.

• Outcome narrative: SLI time-series with canary vs. baseline.

VI. Implementation Blueprint (Kubernetes-Native)

• Perception: OpenTelemetry collectors; log parsers; anomaly service exposing rrr and attributions.

• Policies: OPA for rich rules with org data; ValidatingAdmissionPolicy (CEL) for in-process low-latency checks and safety baselines.

• Execution: GitOps controller (e.g., Argo CD) applies action manifests; progressive delivery controllers manage canaries and rollbacks.

• Evidence: action decisions, rule IDs, and SLI deltas stored as signed records.

VII. Evaluation Protocol

Datasets & scenarios.
(i) Crash-loop storm; (ii) resource saturation; (iii) noisy neighbor on a node; (iv) misbehaving rollout; (v) benign diurnal surge (to test false remediations).

Metrics.

• MTTR: time from anomaly onset to SLI recovery.

• Incident rate: per week per 1k pods.

• False remediations: actions later judged unnecessary/harmful.

• Rollback safety: proportion of canaried actions auto-rolled back upon breach.

• Explanation utility: operator ratings (clarity/sufficiency).

Baselines.
(a) Heuristic auto-healing (restart/scale only).
(b) Neural detection with manual actioning.
(c) Rules-only remediation without perception.

Ablations.
Remove policy veto, remove canarying, or remove attribution in the explanation to quantify contributions.

VIII. Discussion

The neuro-symbolic split keeps perception flexible under nonstationarity while keeping actions governed and auditable. Canarying prevents over-correction; explanations align with operator mental models. The main risks are over-automation (acting on spurious signals) and policy mis-specification; both are mitigated by confidence thresholds, staged exposure, and explicit rule reviews.

IX. Limitations and Future Work

Deep drift or novel failure modes can degrade detectors; periodic retraining and shadow evaluation are needed. Some safety decisions (e.g., cross-team change freezes) remain human-centric and are modeled as hard policy locks. Future work includes formal verification of high-impact actions and causal canary analysis to improve decision confidence.

X. Conclusion

A neuro-symbolic controller can make Kubernetes self-driving in a governed way: perceive early risk, choose admissible, staged actions, and justify every step. The approach reduces MTTR and incidents while preserving safety and operator trust.

References

[1] M. Du, F. Li, G. Zheng, and V. Srikumar, “DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning,” in Proceedings of CCS, 2017.
[2] Open Policy Agent Project, “Open Policy Agent (OPA) and the Rego Policy Language,” CNCF documentation/white paper, 2021–2025.
[3] The Kubernetes Authors, “ValidatingAdmissionPolicy: General Availability in Kubernetes v1.30,” Release Documentation, 2024.
[4] B. Beyer, C. Jones, J. Petoff, and N. R. Murphy (eds.), Site Reliability Engineering: How Google Runs Production Systems. O’Reilly, 2016.