extract: 2025-04-25-bournassenko-queueing-theory-cicd-pipelines

Pentagon-Agent: Ganymede <F99EBFA6-547B-4096-BEEA-1D59C3E4028A>

domains/internet-finance/aimd-worker-scaling-requires-only-queue-state-observation-not-load-prediction-making-it-simpler-than-ml-based-autoscaling.md

@@ -31,6 +31,12 @@ For the Teleo pipeline specifically: when extract produces claims faster than ev
 
 The tradeoff: AIMD is reactive rather than predictive, so it responds to load changes rather than anticipating them. For bursty workloads with predictable patterns, ML-based prediction might provision capacity faster. But for unpredictable workloads or systems where prediction accuracy is low, AIMD's simplicity and guaranteed stability are compelling.
 
+
+### Additional Evidence (extend)
+*Source: [[2025-04-25-bournassenko-queueing-theory-cicd-pipelines]] | Added: 2026-03-15*
+
+Bournassenko's queueing theory framework provides the analytical foundation for why queue-state-based scaling works: M/M/c models give closed-form solutions for optimal worker counts based on observed arrival rates and queue depths, eliminating the need for predictive models.
+
 ---
 
 Relevant Notes:

domains/internet-finance/littles-law-provides-minimum-worker-capacity-floor-for-pipeline-systems-but-requires-buffer-margin-for-variance.md

@@ -28,6 +28,12 @@ For Teleo pipeline: if processing ~8 sources per extraction cycle (every 5 min)
 
 More generally: λ = average sources per second, W = average extraction time. Total workers needed ≥ λ × W gives the minimum worker floor. Additional capacity rules (like square-root staffing) provide the safety margin above that floor.
 
+
+### Additional Evidence (extend)
+*Source: [[2025-04-25-bournassenko-queueing-theory-cicd-pipelines]] | Added: 2026-03-15*
+
+Bournassenko's M/M/c framework extends Little's Law by providing closed-form solutions for the buffer margin required. While Little's Law gives the minimum capacity floor (L = λW), M/M/c queues quantify how much additional capacity is needed to handle variance, showing that the safety margin grows sublinearly with system size.
+
 ---
 
 Relevant Notes:

domains/internet-finance/multi-server-queueing-systems-exhibit-economies-of-scale-because-safety-margin-grows-sublinearly-with-system-size.md

@@ -27,6 +27,12 @@ Ward Whitt presents this as a fundamental result from multi-server queueing anal
 
 This is observable in practice across industries: Amazon's fulfillment centers, telecom networks, and financial trading systems all exhibit this scaling behavior.
 
+
+### Additional Evidence (confirm)
+*Source: [[2025-04-25-bournassenko-queueing-theory-cicd-pipelines]] | Added: 2026-03-15*
+
+The paper demonstrates this principle specifically for CI/CD pipelines, showing that M/M/c models reveal diminishing returns to worker scaling where marginal improvement of worker N+1 decreases as N grows, confirming the sublinear safety margin growth.
+
 ---
 
 Relevant Notes:

inbox/archive/.extraction-debug/2025-04-25-bournassenko-queueing-theory-cicd-pipelines.json

@@ -0,0 +1,40 @@
+{
+  "rejected_claims": [
+    {
+      "filename": "m-m-c-queueing-models-provide-closed-form-solutions-for-ci-cd-pipeline-optimization-because-poisson-arrivals-and-exponential-service-enable-analytical-tractability.md",
+      "issues": [
+        "missing_attribution_extractor"
+      ]
+    },
+    {
+      "filename": "worker-scaling-exhibits-diminishing-returns-in-m-m-c-queues-because-marginal-wait-time-reduction-decreases-as-server-count-increases.md",
+      "issues": [
+        "missing_attribution_extractor"
+      ]
+    },
+    {
+      "filename": "dynamic-worker-allocation-in-pipelines-requires-balancing-queue-depth-against-worker-cost-because-underprovisioning-creates-bottlenecks-while-overprovisioning-wastes-compute.md",
+      "issues": [
+        "missing_attribution_extractor"
+      ]
+    }
+  ],
+  "validation_stats": {
+    "total": 3,
+    "kept": 0,
+    "fixed": 3,
+    "rejected": 3,
+    "fixes_applied": [
+      "m-m-c-queueing-models-provide-closed-form-solutions-for-ci-cd-pipeline-optimization-because-poisson-arrivals-and-exponential-service-enable-analytical-tractability.md:set_created:2026-03-15",
+      "worker-scaling-exhibits-diminishing-returns-in-m-m-c-queues-because-marginal-wait-time-reduction-decreases-as-server-count-increases.md:set_created:2026-03-15",
+      "dynamic-worker-allocation-in-pipelines-requires-balancing-queue-depth-against-worker-cost-because-underprovisioning-creates-bottlenecks-while-overprovisioning-wastes-compute.md:set_created:2026-03-15"
+    ],
+    "rejections": [
+      "m-m-c-queueing-models-provide-closed-form-solutions-for-ci-cd-pipeline-optimization-because-poisson-arrivals-and-exponential-service-enable-analytical-tractability.md:missing_attribution_extractor",
+      "worker-scaling-exhibits-diminishing-returns-in-m-m-c-queues-because-marginal-wait-time-reduction-decreases-as-server-count-increases.md:missing_attribution_extractor",
+      "dynamic-worker-allocation-in-pipelines-requires-balancing-queue-depth-against-worker-cost-because-underprovisioning-creates-bottlenecks-while-overprovisioning-wastes-compute.md:missing_attribution_extractor"
+    ]
+  },
+  "model": "anthropic/claude-sonnet-4.5",
+  "date": "2026-03-15"
+}

inbox/archive/2025-04-25-bournassenko-queueing-theory-cicd-pipelines.md

@@ -6,8 +6,12 @@ url: https://arxiv.org/abs/2504.18705
 date: 2025-04-25
 domain: internet-finance
 format: paper
-status: unprocessed
+status: enrichment
 tags: [pipeline-architecture, operations-research, queueing-theory, ci-cd, M/M/c-queue]
+processed_by: rio
+processed_date: 2026-03-15
+enrichments_applied: ["littles-law-provides-minimum-worker-capacity-floor-for-pipeline-systems-but-requires-buffer-margin-for-variance.md", "multi-server-queueing-systems-exhibit-economies-of-scale-because-safety-margin-grows-sublinearly-with-system-size.md", "aimd-worker-scaling-requires-only-queue-state-observation-not-load-prediction-making-it-simpler-than-ml-based-autoscaling.md"]
+extraction_model: "anthropic/claude-sonnet-4.5"
 ---
 
 # On Queueing Theory for Large-Scale CI/CD Pipelines Optimization
@@ -27,3 +31,9 @@ Academic paper applying classical M/M/c queueing theory to model CI/CD pipeline
 Direct parallel: our extract/eval pipeline IS a multi-stage CI/CD-like system. Sources arrive (Poisson-ish), workers process them (variable service times), and queue depth determines throughput. The M/M/c framework gives us closed-form solutions for expected wait times given worker counts.
 
 Key insight: M/M/c queues show that adding workers has diminishing returns — the marginal improvement of worker N+1 decreases as N grows. This means there's an optimal worker count beyond which additional workers waste compute without meaningfully reducing queue wait times.
+
+
+## Key Facts
+- Bournassenko's paper was published on arxiv.org in April 2025
+- The paper models pipeline stages as M/M/c queues with Poisson arrivals, exponential service times, and c servers
+- The framework addresses bottleneck formation in high-volume shared infrastructure pipelines