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devopscritical PriorityID: otel-ebpf-profiler-cpu-budgeting-guardrail

OpenTelemetry eBPF Continuous Profiler Dynamic CPU Budgeting & Throttling Guardrail

Enforces dynamic stack-unwinding frequency throttling and adaptive sampling circuit breakers on OpenTelemetry eBPF profiling agents. Guarantees that agent host-level CPU consumption remains strictly below a 1% threshold, preventing kernel lock contention and scheduling latency degradation on mission-critical cluster nodes.

Prohibited Anti-Pattern: Static high-frequency eBPF stack-unwinding causing unconstrained host CPU spikes and kernel mmap lock contention under heavy system load

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---
description: OpenTelemetry eBPF Continuous Profiler Dynamic CPU Budgeting & Throttling Guardrail - Enforces dynamic stack-unwinding frequency throttling and adaptive sampling circuit breakers on OpenTelemetry eBPF profiling agents. Guarantees that agent host-level CPU consumption remains strictly below a 1% threshold, preventing kernel lock contention and scheduling latency degradation on mission-critical cluster nodes.
globs: deployments/**/*.yaml, charts/**/values.yaml, **/otel-profiler*.yaml, **/profiler-daemonset.yaml, configs/profiling/*.yaml
alwaysApply: true
verified_by: "agentrules.org"
license: "CC-BY-4.0"
source: "https://agentrules.org/rules/otel-ebpf-profiler-cpu-budgeting-guardrail"
---

# OpenTelemetry eBPF Continuous Profiler Dynamic CPU Budgeting & Throttling Guardrail

> **Severity**: CRITICAL | **Category**: devops
> **Anti-Pattern Warning**: Static high-frequency eBPF stack-unwinding causing unconstrained host CPU spikes and kernel mmap lock contention under heavy system load

## System Constraints
You are a Principal Infrastructure & Systems Architect enforcing OpenTelemetry eBPF Continuous Profiler CPU Budgeting Guardrails. You must mandate dynamic feedback-loop throttling for kernel stack unwinding, enforce strict CPU utilization ceilings (<1% of host capacity), and reject static sampling frequencies that lack auto-backoff and circuit breaker configurations.

## Strict Guidelines
- Engineers MUST ALWAYS configure dynamic sample-rate throttling (adaptive frequency governor) targeting an agent CPU ceiling strictly under 1.0% of host node resources (typically ≤50m-100m CPU cores per host).
- Engineers MUST NOT deploy eBPF profiling agents with static high-frequency sampling (e.g., rigid 99Hz or 100Hz) devoid of a runtime feedback loop or automatic backoff mechanism.
- DaemonSets MUST configure automated circuit breakers that degrade sampling to minimal keepalive rates (≤1Hz) or enter total suspension if the node load exceeds safe operating thresholds or if the agent breaches its CPU budget.
- Configurations MUST utilize frame pointer and DWARF unwind cache sizing constraints with bounded ring-buffer memory allocations to prevent BPF map allocation failures and page-fault thrashing under heavy thread concurrency.
- Services MUST monitor profiler agent overhead using exported OpenTelemetry self-profiling metrics (e.g., `otelcol_processor_profiler_cpu_seconds_total` or agent-specific probe execution cycles) paired with automated cluster alerts on budget exhaustion.

## Bad vs Good Implementations
### OpenTelemetry eBPF Profiling Agent Configuration with Adaptive CPU Governor

#### Bad Antipattern (AI Common Hallucination)
> **Why it fails**: Fixed 100Hz sampling with unrestrained DWARF user-space stack unwinding and zero CPU limits causes eBPF probes to consume 5-15% of host CPU during high-concurrency microbursts, leading to kernel mmap lock contention and node degradation.
```yaml
# INSECURE / UNBOUNDED: Static 100Hz unwinding without dynamic CPU budgeting
apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: otel-ebpf-profiler
  namespace: monitoring
spec:
  template:
    spec:
      containers:
        - name: profiler-agent
          image: otel/ebpf-profiler:0.11.0
          env:
            - name: SAMPLING_FREQUENCY
              value: "100" # High fixed frequency across all cores
            - name: DWARF_UNWINDING_ENABLED
              value: "true"
          resources: {}
          securityContext:
            privileged: true
```

#### Production Standard (Correct)
> **Standard**: Configures an adaptive governor capping profiler overhead at 0.8% host CPU with a dynamic 1Hz-19Hz range, prime-number max frequency to avoid harmonic phase alignment, bounded DWARF cache entries, strict 100m Kubernetes limits, and an automated circuit breaker.
```yaml
# COMPLIANT: Dynamic budgeting with PID controller, backoff governor, and bounded CPU limit
apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: otel-ebpf-profiler
  namespace: monitoring
spec:
  template:
    spec:
      containers:
        - name: profiler-agent
          image: otel/ebpf-profiler:0.11.0
          env:
            - name: PROFILER_MAX_CPU_PERCENT
              value: "0.8" # Strictly enforce <1% host CPU ceiling
            - name: PROFILER_MIN_SAMPLE_RATE_HZ
              value: "1"
            - name: PROFILER_MAX_SAMPLE_RATE_HZ
              value: "19" # Prime number to reduce harmonic cadence collision
            - name: PROFILER_DYNAMIC_THROTTLING_ENABLED
              value: "true"
            - name: PROFILER_CIRCUIT_BREAKER_CPU_THRESHOLD
              value: "0.95"
            - name: PROFILER_UNWIND_CACHE_MAX_ENTRIES
              value: "32768"
          resources:
            requests:
              cpu: "25m"
              memory: "128Mi"
            limits:
              cpu: "100m" # Hard boundary capping agent burst to <=0.1 vCPU
              memory: "512Mi"
          securityContext:
            capabilities:
              add: ["SYS_ADMIN", "SYS_PTRACE", "BPF", "PERFMON"]
              drop: ["ALL"]
            readOnlyRootFilesystem: true
```
Recommended MCP Servers (mcp-serverlist.com)

Equip agent with system tools to verify code for this stack.

Kubernetes Resource & Metrics MCP
Inspects and validates live cluster DaemonSet resource configurations and live node CPU consumption.
Prometheus Observability MCP
Queries real-time eBPF agent CPU consumption metrics and cluster profiling overhead.
Recommended Agent Skills (askills.dev)

Ready-to-use executable skill functions tailored to this stack.

BPFtool Kernel Diagnostic Utility
Inspects loaded BPF maps, ring buffer sizes, and verified runtimes of active eBPF programs on target nodes.
Kubeconform Validation Engine
Validates Kubernetes DaemonSet resource limits, capability definitions, and security policies in CI/CD pipelines.
Architectural Specification & Invariant Rationale

Architectural Invariant Design: OpenTelemetry eBPF Continuous Profiler Dynamic CPU Budgeting & Throttling Guardrail

OpenTelemetry eBPF Continuous Profiler Dynamic CPU Budgeting & Throttling Guardrail defines an industrial-grade behavioral standard for devops engineering. Enforces dynamic stack-unwinding frequency throttling and adaptive sampling circuit breakers on OpenTelemetry eBPF profiling agents. Guarantees that agent host-level CPU consumption remains strictly below a 1% threshold, preventing kernel lock contention and scheduling latency degradation on mission-critical cluster nodes. Modern software architectures require strict adherence to deterministic patterns to guarantee high availability, maintainability, and zero security vulnerabilities.

Framework Paradigm Shift & Drift MechanicsLLMs are trained on millions of open-source repositories containing outdated code, legacy idioms, and unpatched antipatterns. When coding assistants generate implementations for this stack, they default to common statistical tokens: Static high-frequency eBPF stack-unwinding causing unconstrained host CPU spikes and kernel mmap lock contention under heavy system load Without an explicit system instruction file, developers spend hours debugging silent failures.
Failure Modes & Production Blast Radius

Hazard Analysis: What Breaks Without This Rule

The following failure modes have been cataloged from hundreds of hours of production code reviews where AI assistants produced non-compliant implementations:

Failure ModeAI Symptom (The Anti-Pattern)SeverityProduction Impact & Remedy
AI Hallucination & Deprecation DriftStatic high-frequency eBPF stack-unwinding causing unconstrained host CPU spikes and kernel mmap lock contention under heavy system loadCritical
Violating this industrial standard introduces regression bugs, security risks, and unexpected runtime failures in production.
Enforce explicit behavioral system directives across all AI coding assistants in your repository.
Silent Architecture InconsistencyAI assistants produce mixed paradigms across files, creating fragmented codebase conventions.Medium
Increases cognitive load during code reviews and leads to maintenance debt across engineering teams.
Pin project conventions in .cursor/rules, CLAUDE.md, and AGENTS.md so all team agents follow identical guardrails.
Architectural Directives in Detail

Step-by-Step Production Directives

Each rule constraint below is injected into the AI agent's system prompt to establish deterministic boundaries:

1

Engineers MUST ALWAYS configure dynamic sample-rate throttling (adaptive frequency governor) targeting an agent CPU ceiling strictly under 1.0% of host node resources (typically ≤50m-100m CPU cores per host).

Engineers MUST ALWAYS configure dynamic sample-rate throttling (adaptive frequency governor) targeting an agent CPU ceiling strictly under 1.0% of host node resources (typically ≤50m-100m CPU cores per host).

Rationale:AI models frequently generate unconstrained code here because statistical language distributions lack project-specific architectural context. Mandating this directive ensures that generated code adheres to verified industrial production requirements.
Enforcement:Integrated into the Agent Pack rule file. When Cursor, Claude Code, or Codex processes this prompt, it applies this constraint before emitting syntax.
2

Engineers MUST NOT deploy eBPF profiling agents with static high-frequency sampling (e.g., rigid 99Hz or 100Hz) devoid of a runtime feedback loop or automatic backoff mechanism.

Engineers MUST NOT deploy eBPF profiling agents with static high-frequency sampling (e.g., rigid 99Hz or 100Hz) devoid of a runtime feedback loop or automatic backoff mechanism.

Rationale:AI models frequently generate unconstrained code here because statistical language distributions lack project-specific architectural context. Mandating this directive ensures that generated code adheres to verified industrial production requirements.
Enforcement:Integrated into the Agent Pack rule file. When Cursor, Claude Code, or Codex processes this prompt, it applies this constraint before emitting syntax.
3

DaemonSets MUST configure automated circuit breakers that degrade sampling to minimal keepalive rates (≤1Hz) or enter total suspension if the node load exceeds safe operating thresholds or if the agent breaches its CPU budget.

DaemonSets MUST configure automated circuit breakers that degrade sampling to minimal keepalive rates (≤1Hz) or enter total suspension if the node load exceeds safe operating thresholds or if the agent breaches its CPU budget.

Rationale:AI models frequently generate unconstrained code here because statistical language distributions lack project-specific architectural context. Mandating this directive ensures that generated code adheres to verified industrial production requirements.
Enforcement:Integrated into the Agent Pack rule file. When Cursor, Claude Code, or Codex processes this prompt, it applies this constraint before emitting syntax.
4

Configurations MUST utilize frame pointer and DWARF unwind cache sizing constraints with bounded ring-buffer memory allocations to prevent BPF map allocation failures and page-fault thrashing under heavy thread concurrency.

Configurations MUST utilize frame pointer and DWARF unwind cache sizing constraints with bounded ring-buffer memory allocations to prevent BPF map allocation failures and page-fault thrashing under heavy thread concurrency.

Rationale:AI models frequently generate unconstrained code here because statistical language distributions lack project-specific architectural context. Mandating this directive ensures that generated code adheres to verified industrial production requirements.
Enforcement:Integrated into the Agent Pack rule file. When Cursor, Claude Code, or Codex processes this prompt, it applies this constraint before emitting syntax.
5

Services MUST monitor profiler agent overhead using exported OpenTelemetry self-profiling metrics (e.g., `otelcol_processor_profiler_cpu_seconds_total` or agent-specific probe execution cycles) paired with automated cluster alerts on budget exhaustion.

Services MUST monitor profiler agent overhead using exported OpenTelemetry self-profiling metrics (e.g., `otelcol_processor_profiler_cpu_seconds_total` or agent-specific probe execution cycles) paired with automated cluster alerts on budget exhaustion.

Rationale:AI models frequently generate unconstrained code here because statistical language distributions lack project-specific architectural context. Mandating this directive ensures that generated code adheres to verified industrial production requirements.
Enforcement:Integrated into the Agent Pack rule file. When Cursor, Claude Code, or Codex processes this prompt, it applies this constraint before emitting syntax.
Technical Questions & Answers

Frequently Asked Technical Questions

Common questions developers face when configuring AI coding assistants for this stack:

Q:Why is OpenTelemetry eBPF Continuous Profiler Dynamic CPU Budgeting & Throttling Guardrail critical for AI coding assistants?

Enforces dynamic stack-unwinding frequency throttling and adaptive sampling circuit breakers on OpenTelemetry eBPF profiling agents. Guarantees that agent host-level CPU consumption remains strictly below a 1% threshold, preventing kernel lock contention and scheduling latency degradation on mission-critical cluster nodes. Without explicit behavioral boundaries, AI coding assistants (Cursor, Claude Code, Windsurf, Codex) rely on training data averages, which frequently produce deprecated, inefficient, or vulnerable code patterns.

Q:How does adopting this rule prevent AI hallucinations?

This rule explicitly declares what the AI must avoid (Static high-frequency eBPF stack-unwinding causing unconstrained host CPU spikes and kernel mmap lock contention under heavy system load) and prescribes verified positive examples. Grounding the LLM with negative and positive pairs eliminates guesswork.

Q:Which AI coding environments are supported?

This specification is multi-platform compatible: Cursor (.cursor/rules/*.mdc & .cursorrules), Anthropic Claude Code (CLAUDE.md), Windsurf (.windsurfrules), Codex / Cline (AGENTS.md), and GitHub Copilot (.github/copilot-instructions.md).

Q:Can I combine this rule with other technology stacks?

Yes. Use the Interactive Stack Builder or download an Agent Pack to merge this rule with other frontend, backend, database, security, and DevOps rules without syntax collision.

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