Add scheduler fairness summary to sustained feeds

docs/agent_orchestration_summary_contract.ko.md

@@ -140,6 +140,11 @@ Top-level summary:
   scheduler-delay signal, affected task, worker health를 compact supplemental
   risk context로 요약해 AIGuard/Lab/EdgeEnv가 사용할 수 있게 합니다.
   `decision_owner=lab`과 `not_a_deployment_decision=true`를 명시합니다.
+- `scheduler_fairness_summary`: sustained operation evidence 안의 additive
+  context로, 보호된 high-priority task, scheduler-delay 또는 starvation risk가
+  있는 task, degraded/constrained worker, task별 fairness context를
+  요약합니다. Orchestrator가 소유하는 supplemental scheduling evidence이며
+  Lab deployment decision이나 EdgeEnv regression judgement가 아닙니다.
 - `runtime_event_timeline`: queue snapshot, drop, scheduler selection,
   execution, policy decision, resource snapshot을 순서대로 남기는 event log입니다.
   execution event는 backlog/delay 확인을 위한 additive

docs/agent_orchestration_summary_contract.md

@@ -140,6 +140,11 @@ Additive operation-health fields:
   counts, scheduler-delay signals, affected tasks, and worker health into a
   compact supplemental risk context for AIGuard/Lab/EdgeEnv. It explicitly
   records `decision_owner=lab` and `not_a_deployment_decision=true`.
+- `scheduler_fairness_summary`: additive context inside sustained operation
+  evidence that summarizes protected high-priority tasks, tasks with
+  scheduler-delay or starvation risk, degraded/constrained workers, and
+  per-task fairness context. It is Orchestrator-owned supplemental scheduling
+  evidence, not a Lab deployment decision or EdgeEnv regression judgement.
 - `runtime_event_timeline`: ordered event log for queue snapshots, drops,
   scheduler selections, executions, policy decisions, and resource snapshots.
   Execution events include additive `scheduler_delay_cycles` and

examples/telemetry/README.ko.md

@@ -15,7 +15,7 @@ portfolio evidence를 확인할 수 있도록 제공한다.
 | Question | Start with | Why |
 | --- | --- | --- |
 | scheduler가 overload에서 high-priority work를 보호하는가? | `phase3_overload_sample.json` | baseline vs scheduled p95 latency와 low-priority drop을 보여준다. |
-| 3-agent sustained path가 downstream review용 scheduler-delay evidence를 기록하는가? | `agent_scheduler_delay_sample.json` | 지연된 execution, policy/drop reason count, AIGuard/Lab signal name을 보여준다. |
+| 3-agent sustained path가 downstream review용 scheduler-delay evidence와 fairness context를 기록하는가? | `agent_scheduler_delay_sample.json` | 지연된 execution, scheduler fairness/starvation context, policy/drop reason count, AIGuard/Lab signal name을 보여준다. |
 | remote dispatch starter evidence가 production retry control을 주장하지 않고 bounded fallback recovery를 보여주는가? | `remote_fallback_recovery_sample.json` | primary failure, fallback recovery, compact runtime event summary, starter boundary field를 보여준다. |
 | 어떤 sample이 local CI가 아니라 기존 Jetson evidence를 전제로 하는가? | `jetson_*_sample.json` files | portable CI output이 아니라 curated physical-device 또는 TensorRT-backed evidence snapshot이다. |
 
@@ -24,7 +24,7 @@ portfolio evidence를 확인할 수 있도록 제공한다.
 | File | What it shows |
 | --- | --- |
 | `phase3_overload_sample.json` | synthetic FIFO baseline과 scheduler/load-shedding 비교. detector p95 end-to-end latency가 `782.0ms`에서 `8.0ms`로 개선되고 low-priority classifier work가 drop된다. |
-| `agent_scheduler_delay_sample.json` | 3-agent sustained high-load config에서 추출한 curated excerpt. `scheduler_delay_event_count`, 지연된 execution event, policy/drop reason count, downstream AIGuard/Lab signal name을 보여준다. |
+| `agent_scheduler_delay_sample.json` | 3-agent sustained high-load config에서 추출한 curated excerpt. `scheduler_delay_event_count`, `scheduler_fairness_summary`, 지연된 execution event, policy/drop reason count, downstream AIGuard/Lab signal name을 보여준다. |
 | `remote_fallback_recovery_sample.json` | remote dispatch starter에서 primary HTTP starter `connection_error`, 제한된 fallback worker recovery, retry/fallback plan field, downstream AIGuard/Lab signal name을 보여주는 curated excerpt. |
 | `jetson_smoke_dummy_sample.json` | Jetson dummy smoke path의 telemetry schema. task count, drop event, result event, scheduler decision, resource snapshot을 보여준다. |
 | `jetson_onnx_smoke_sample.json` | ONNX Runtime worker smoke path의 telemetry schema. result event metadata, output shape `[1, 2]`, resource snapshot을 보여준다. |
@@ -41,6 +41,7 @@ sample은 다음 telemetry signal을 포함한다.
 - drop events
 - overload 또는 policy decisions
 - scheduler delay event count와 queue wait evidence
+- scheduler fairness / starvation context
 - remote dispatch starter failure/fallback recovery evidence
 - result events
 - resource snapshots

examples/telemetry/README.md

@@ -15,7 +15,7 @@ They are not benchmark artifacts. Raw runtime reports remain ignored under
 | Question | Start with | Why |
 | --- | --- | --- |
 | Does the scheduler protect high-priority work under overload? | `phase3_overload_sample.json` | Shows baseline vs scheduled p95 latency and low-priority drops. |
-| Did the 3-agent sustained path record scheduler-delay evidence for downstream review? | `agent_scheduler_delay_sample.json` | Shows delayed execution, policy/drop reason counts, and AIGuard/Lab signal names. |
+| Did the 3-agent sustained path record scheduler-delay evidence and fairness context for downstream review? | `agent_scheduler_delay_sample.json` | Shows delayed execution, scheduler fairness/starvation context, policy/drop reason counts, and AIGuard/Lab signal names. |
 | Does remote dispatch starter evidence show bounded fallback recovery without claiming production retry control? | `remote_fallback_recovery_sample.json` | Shows primary failure, fallback recovery, compact runtime event summary, and starter boundary fields. |
 | Which samples require prior Jetson evidence rather than local CI? | `jetson_*_sample.json` files | They are curated physical-device or TensorRT-backed evidence snapshots, not portable CI outputs. |
 
@@ -24,7 +24,7 @@ They are not benchmark artifacts. Raw runtime reports remain ignored under
 | File | What it shows |
 | --- | --- |
 | `phase3_overload_sample.json` | Synthetic FIFO baseline vs scheduler/load-shedding comparison. The detector p95 end-to-end latency improves from `782.0ms` to `8.0ms`, while low-priority classifier work is dropped. |
-| `agent_scheduler_delay_sample.json` | Curated excerpt from the 3-agent sustained high-load config showing `scheduler_delay_event_count`, a delayed execution event, policy/drop reason counts, and the downstream AIGuard/Lab signal names. |
+| `agent_scheduler_delay_sample.json` | Curated excerpt from the 3-agent sustained high-load config showing `scheduler_delay_event_count`, `scheduler_fairness_summary`, a delayed execution event, policy/drop reason counts, and the downstream AIGuard/Lab signal names. |
 | `remote_fallback_recovery_sample.json` | Curated remote dispatch starter excerpt showing primary HTTP starter `connection_error`, bounded fallback worker recovery, retry/fallback plan fields, and downstream AIGuard/Lab signal names. |
 | `jetson_smoke_dummy_sample.json` | Telemetry schema from the Jetson dummy smoke path: task counts, drop events, result events, scheduler decisions, and resource snapshots. |
 | `jetson_onnx_smoke_sample.json` | Telemetry schema from the ONNX Runtime worker smoke path: result event metadata, output shape `[1, 2]`, and resource snapshots. |
@@ -41,6 +41,7 @@ The samples cover these telemetry signals:
 - drop events
 - overload or policy decisions
 - scheduler delay event counts and queue wait evidence
+- scheduler fairness / starvation context
 - remote dispatch starter failure/fallback recovery evidence
 - result events
 - resource snapshots

examples/telemetry/agent_scheduler_delay_sample.json

@@ -29,6 +29,68 @@
       "queue_overflow_drop_oldest": 12
     }
   },
+  "scheduler_fairness_summary": {
+    "schema_version": "inferedge-orchestrator-scheduler-fairness-summary-v1",
+    "operation_context_role": "supplemental",
+    "scheduler_owner": "orchestrator",
+    "decision_owner": "lab",
+    "not_a_deployment_decision": true,
+    "protected_high_priority_tasks": [
+      "safety_monitor_agent"
+    ],
+    "tasks_with_starvation_risk": [
+      "vision_agent"
+    ],
+    "tasks_with_scheduler_delay": [
+      "vision_agent"
+    ],
+    "tasks_with_degradation": [
+      "voice_command_agent"
+    ],
+    "first_read": "review_scheduler_fairness_context",
+    "task_fairness": {
+      "vision_agent": {
+        "priority": 80,
+        "executed_count": 3,
+        "dropped_count": 12,
+        "fallback_count": 0,
+        "scheduler_delay_event_count": 3,
+        "max_scheduler_delay_cycles": 3,
+        "health_state": "constrained",
+        "starvation_risk": true,
+        "starvation_reasons": [
+          "scheduler_delay_present",
+          "multi_cycle_scheduler_delay"
+        ]
+      },
+      "voice_command_agent": {
+        "priority": 50,
+        "executed_count": 1,
+        "dropped_count": 9,
+        "fallback_count": 9,
+        "scheduler_delay_event_count": 0,
+        "max_scheduler_delay_cycles": 0,
+        "health_state": "degraded",
+        "starvation_risk": true,
+        "starvation_reasons": [
+          "fallback_policy_used",
+          "worker_degraded"
+        ]
+      },
+      "safety_monitor_agent": {
+        "priority": 100,
+        "executed_count": 16,
+        "dropped_count": 0,
+        "fallback_count": 0,
+        "scheduler_delay_event_count": 0,
+        "max_scheduler_delay_cycles": 0,
+        "health_state": "healthy",
+        "starvation_risk": false,
+        "starvation_reasons": []
+      }
+    },
+    "interpretation": "Scheduler fairness is supplemental operation evidence; Lab remains the final deployment decision owner."
+  },
   "delayed_execution_sample": {
     "event_type": "execution",
     "agent_id": "vision_agent",

scripts/check_edgeenv_runtime_feed_contract.py

@@ -111,6 +111,9 @@ def main(argv: list[str] | None = None) -> int:
         affected = operation_timeline_summary.get("affected_tasks") or {}
         review_hints = operation_timeline_summary.get("review_hints") or []
         stale_drop = operation_timeline_summary.get("stale_drop") or {}
+        scheduler_fairness = operation_timeline_summary.get(
+            "scheduler_fairness"
+        ) or {}
         print(
             "operation_timeline: "
             f"review_hints={_format_list(review_hints)}; "
@@ -121,6 +124,16 @@ def main(argv: list[str] | None = None) -> int:
             f"stale_drop_tasks={_format_list(affected.get('stale_drop'))}; "
             f"max_queue_wait_ms={latency.get('max_queue_wait_ms', 0)}"
         )
+        if scheduler_fairness:
+            print(
+                "scheduler_fairness: "
+                "protected="
+                f"{_format_list(scheduler_fairness.get('protected_high_priority_tasks'))}; "
+                "starvation_risk="
+                f"{_format_list(scheduler_fairness.get('tasks_with_starvation_risk'))}; "
+                "degraded="
+                f"{_format_list(scheduler_fairness.get('tasks_with_degradation'))}"
+            )
     return 0