Tuning Approval Thresholds by Measured Cost

#Define Workloads and Incident Classes to Measure the Tradeoff Between Slower Human Approvals, False Positive Auto‑Fixes, and Missed Remediation Windows

Introduction to Workloads and Incident Classes

Defining Workloads

A workload is the observable demand placed on a system by users, applications, or internal processes over a defined time interval. In network‑centric observability a workload can be expressed as the tuple

W = (λ, σ, ν)

• λ (arrival rate) – average number of work items per second (e.g., packets, flows, API calls).
• σ (size distribution) – statistical description of resource consumption per item (e.g., packet size, CPU cycles, memory footprint).
• ν (variety vector) – categorical attributes that differentiate work items (e.g., protocol, tenant ID, application tag, geographic region).

Workloads are measured at the data‑plane (e.g., gNMI telemetry of interface counters) and the control‑plane (e.g., BGP update rate, telemetry subscription load). Defining workloads enables quantitative comparison of “how much” work the system must handle versus “how fast” it can react to incidents.

Categorizing Incident Classes

An incident class groups failures that share a common root cause, impact profile, and remediation path. Typical network‑oriented incident classes include:

Each class receives a severity (impact on SLA), priority (order of handling), and an expected remediation window (maximum allowable time to restore service before SLA breach).

Understanding the Tradeoff

Human Approvals: Benefits and Drawbacks

Benefits

• Contextual judgment – Operators apply policy nuances static rules miss (e.g., approving a bandwidth increase for a known burst).
• Auditability – Manual approval creates an immutable trail (ticket comments, timestamps) useful for compliance.
• Risk mitigation – Humans catch edge‑cases where automated logic would cause service degradation (false‑positive auto‑fix).

Drawbacks

• Latency – Mean Time To Approve (MTTA) ranges from seconds (chat‑ops) to hours (ticket‑based approvals), extending the incident lifecycle.
• Inconsistency – Shift‑to‑shift variability introduces noise in decision latency and correctness.
• Scalability ceiling – Human throughput is bounded by cognitive load; beyond ~10‑20 approvals/min per engineer, queues build up.

Auto‑Fixes: False Positives and Their Impact

Auto‑fix mechanisms execute remediation without waiting for human consent. Performance is measured by:

• Precision (Positive Predictive Value) = TP / (TP + FP) – proportion of auto‑fixes that were truly needed.
• Recall (Sensitivity) = TP / (TP + FN) – proportion of actual incidents that were auto‑remediated.
• False Positive Rate (FPR) = FP / (FP + TN) – rate at which benign states trigger an unnecessary action.

Impact of False Positives

• Service disruption – Unneeded actions (e.g., interface shutdown, ACL tightening) can cause packet loss or latency spikes.
• Alert fatigue – Repeated unnecessary actions generate noise, obscuring genuine incidents.
• Operational cost – Each false positive may require a rollback or manual verification, consuming engineer time that could be spent on true incidents.

Missed Remediation Windows: Consequences and Mitigation

A missed remediation window occurs when the time from incident detection to service restoration exceeds the class‑specific SLA threshold (T_max). Consequences include:

• SLA penalties – Financial credits or contractual breaches.
• Customer‑experience degradation – Increased latency, jitter, or packet loss perceived by end‑users.
• Cascading failures – Unmitigated congestion can trigger secondary incidents (e.g., bufferbloat leading to TCP retransmission storms).

Mitigation Strategies

• Pre‑emptive throttling – Auto‑scale ingress rate before queue depth hits critical threshold.
• Escalation ladders – If MTTA > T_half (half of T_max), automatically elevate to a senior engineer or trigger a higher‑privilege auto‑fix.
• Predictive remediation – Use short‑term forecast (e.g., EWMA of queue depth) to trigger actions before the violation occurs.

Defining Metrics for Evaluation

Workload Metrics: Volume, Velocity, and Variety

These metrics feed into a Workload Intensity Score (WIS):

WIS = α·norm(V) + β·norm(Ṽ) + γ·norm(𝒱)

with α+β+γ=1, tuned per incident class.

Incident Class Metrics: Severity, Priority, and Frequency

A Risk Exposure (RE) can be expressed as:

RE = S × P × F

Higher RE warrants tighter automation thresholds.

Approval and Auto‑Fix Metrics: Accuracy, Speed, and Reliability

These metrics are visualized in a Tradeoff Dashboard (see Implementation section) where axes are MTTA (x) vs. FPR (y) and bubble size encodes RE.

Troubleshooting Common Issues

Identifying Bottlenecks in Human Approval Processes

1. Queue Depth Monitoring – Export approval‑system metrics (e.g., Jira Service Desk approvals_pending) to Prometheus.

   sum by (project) (approvals_pending)

2. Latency Breakdown – Use distributed tracing (OpenTelemetry) on the approval workflow to isolate stages:
   • ticket_creation → triage → decision → notification.
   • Look for spans > 90th percentile.
3. Resource Saturation – Check approver CPU/memory via node exporter; high utilization correlates with increased MTTA.
4. Shift Patterns – Correlate MTTA with approver_on_call label to detect coverage gaps.

Remediation – Introduce auto‑triage rules that route low‑risk incidents to a “fast‑track” queue with pre‑approved actions, reducing load on the human gate.

Debugging Auto‑Fix Algorithms for False Positives

1. Feature Importance – If the auto‑fix uses an ML model, extract SHAP values per decision to see which telemetry features drove the false positive.
2. Threshold Sweep – Run a canary experiment varying the decision threshold (τ) and record FPR/Recall:

   for tau in 0.1 0.2 0.3 0.4 0.5; do
       ./run_autofix --threshold $tau --log results_$tau.json
   done

3. Label Drift Detection – Compare distribution of incoming telemetry (e.g., ifInErrors) against the training set using KL‑divergence; a spike indicates model drift.
4. Shadow Mode – Deploy the auto‑fix in observation‑only mode; log proposed actions and compare with actual incidents to compute precision without affecting traffic.

Remediation – Adjust thresholds, retrain with recent data, or add a rule‑based guardrail (e.g., never shut down an interface if ifOperStatus already down for > 5 min).

Analyzing Missed Remediation Windows for Root Causes

1. Timeline Reconstruction – Join detection timestamps (incident_detected), approval timestamps (approval_given), and remediation timestamps (service_restored) in a SQL or TimescaleDB query:

   SELECT
       i.id,
       EXTRACT(EPOCH FROM (a.approval_time - i.detect_time)) AS mtta,
       EXTRACT(EPOCH FROM (r.restore_time - i.detect_time)) AS mttr,
       i.class,
       i.severity
   FROM incidents i
   LEFT JOIN approvals a ON i.id = a.incident_id
   LEFT JOIN remediations r ON i.id = r.incident_id
   WHERE r.restore_time - i.detect_time > i.t_max;

2. Critical Path Analysis – Identify which stage (detection → approval → action → verification) consumes the majority of excess time.
3. Contention Metrics – Correlate missed windows with system load (CPU, queue depth) at the time of incident using cross‑correlation.
4. Escalation Latency – Measure time from first approval request to escalation trigger; high values indicate ineffective escalation policies.

Remediation – Implement predictive pre‑approval for high‑RE classes, or dynamically lower MTTA thresholds when system load exceeds a configured percentile.

Implementing Workload and Incident Class Management

Code Examples: Integrating Approval and Auto‑Fix Workflows

Below is a minimal Python‑based workflow using Temporal.io (durable execution) that demonstrates a decision gate:

import asyncio
from datetime import timedelta
from temporalio import workflow, activity
from temporalio.client import Client
from temporalio.worker import Worker

# ----- Activities -----
@activity.defn
async def detect_incident(telemetry: dict) -> dict | None:
    # Simplified detection logic
    if telemetry.get("ifInErrors_rate", 0) > 100:
        return {
            "incident_id": "inc-123",
            "class": "LinkFlap",
            "severity": 0.9,
            "ifIndex": telemetry.get("ifIndex"),
        }
    return None


@activity.defn
async def request_human_approval(incident: dict) -> bool:
    # Push to approval system (e.g., ServiceNow) and wait for callback
    await approval_system.submit(incident)
    return await approval_system.wait_for_decision(timeout=300)  # 5 min SLA


@activity.defn
async def execute_auto_fix(incident: dict) -> None:
    if incident["class"] == "LinkFlap":
        await netconf.set_interface_state(incident["ifIndex"], "down")
        await asyncio.sleep(2)
        await netconf.set_interface_state(incident["ifIndex"], "up")


# ----- Workflow -----
@workflow.defn
class IncidentResponse:
    @workflow.run
    async def run(self, telemetry: dict) -> None:
        incident = await workflow.execute_activity(
            detect_incident,
            telemetry,
            start_to_close_timeout=timedelta(seconds=5),
        )
        if not incident:
            return

        # Example decision: auto‑fix for low‑severity, otherwise seek approval
        if incident["severity"] < 0.5:
            await workflow.execute_activity(
                execute_auto_fix,
                incident,
                start_to_close_timeout=timedelta(seconds=10),
            )
        else:
            approved = await workflow.execute_activity(
                request_human_approval,
                incident,
                start_to_close_timeout=timedelta(seconds=300),
            )
            if approved:
                await workflow.execute_activity(
                    execute_auto_fix,
                    incident,
                    start_to_close_timeout=timedelta(seconds=10),
                )
            else:
                # Escalate or manual handling
                await workflow.execute_activity(
                    escalate_incident,
                    incident,
                    start_to_close_timeout=timedelta(seconds=60),
                )

Replace approval_system, netconf, and escalate_incident with your actual integrations.