diff --git a/domains/internet-finance/aimd-worker-scaling-requires-only-queue-state-observation-not-load-prediction-making-it-simpler-than-ml-based-autoscaling.md b/domains/internet-finance/aimd-worker-scaling-requires-only-queue-state-observation-not-load-prediction-making-it-simpler-than-ml-based-autoscaling.md index f84b24187..4673e77d1 100644 --- a/domains/internet-finance/aimd-worker-scaling-requires-only-queue-state-observation-not-load-prediction-making-it-simpler-than-ml-based-autoscaling.md +++ b/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 framework provides the theoretical foundation for why queue-state-based scaling works: M/M/c models show that current queue depth and utilization are sufficient statistics for optimal worker allocation when arrivals are Poisson-like. This explains why AIMD's simple heuristics can approach optimality without complex prediction. + --- Relevant Notes: diff --git a/domains/internet-finance/littles-law-provides-minimum-worker-capacity-floor-for-pipeline-systems-but-requires-buffer-margin-for-variance.md b/domains/internet-finance/littles-law-provides-minimum-worker-capacity-floor-for-pipeline-systems-but-requires-buffer-margin-for-variance.md index c5f4de5d4..7da8ca712 100644 --- a/domains/internet-finance/littles-law-provides-minimum-worker-capacity-floor-for-pipeline-systems-but-requires-buffer-margin-for-variance.md +++ b/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 above the theoretical minimum. The paper shows that the optimal worker count is not just λW (from Little's Law) but includes a variance-dependent safety margin that can be calculated analytically from the M/M/c model. + --- Relevant Notes: diff --git a/domains/internet-finance/multi-server-queueing-systems-exhibit-economies-of-scale-because-safety-margin-grows-sublinearly-with-system-size.md b/domains/internet-finance/multi-server-queueing-systems-exhibit-economies-of-scale-because-safety-margin-grows-sublinearly-with-system-size.md index 984177149..e52104088 100644 --- a/domains/internet-finance/multi-server-queueing-systems-exhibit-economies-of-scale-because-safety-margin-grows-sublinearly-with-system-size.md +++ b/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 large-scale shared infrastructure benefits from pooling effects where the relative safety margin decreases as system size increases. This validates the economies-of-scale claim in a production software context. + --- Relevant Notes: diff --git a/domains/internet-finance/square-root-staffing-principle-achieves-economies-of-scale-in-queueing-systems-by-operating-near-full-utilization-with-manageable-delays.md b/domains/internet-finance/square-root-staffing-principle-achieves-economies-of-scale-in-queueing-systems-by-operating-near-full-utilization-with-manageable-delays.md index e4465c145..66a996611 100644 --- a/domains/internet-finance/square-root-staffing-principle-achieves-economies-of-scale-in-queueing-systems-by-operating-near-full-utilization-with-manageable-delays.md +++ b/domains/internet-finance/square-root-staffing-principle-achieves-economies-of-scale-in-queueing-systems-by-operating-near-full-utilization-with-manageable-delays.md @@ -26,6 +26,12 @@ From the SIAM Review tutorial: For systems in the 5-6 worker range, sophisticated dynamic algorithms provide minimal benefit over simple threshold policies informed by queueing theory. The economies-of-scale result also indicates that marginal value per worker decreases as systems grow beyond 20+ workers, which is critical for cost optimization in scaled deployments. + +### Additional Evidence (confirm) +*Source: [[2025-04-25-bournassenko-queueing-theory-cicd-pipelines]] | Added: 2026-03-15* + +The paper applies square-root staffing principles to CI/CD pipeline optimization, demonstrating that the theoretical framework translates to practical software systems. The M/M/c analysis confirms that safety capacity should scale as √λ for Poisson arrivals. + --- Relevant Notes: diff --git a/inbox/archive/.extraction-debug/2025-04-25-bournassenko-queueing-theory-cicd-pipelines.json b/inbox/archive/.extraction-debug/2025-04-25-bournassenko-queueing-theory-cicd-pipelines.json new file mode 100644 index 000000000..7a18e4342 --- /dev/null +++ b/inbox/archive/.extraction-debug/2025-04-25-bournassenko-queueing-theory-cicd-pipelines.json @@ -0,0 +1,48 @@ +{ + "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-multi-server-queues-because-marginal-wait-time-reduction-decreases-as-server-count-increases.md", + "issues": [ + "missing_attribution_extractor" + ] + }, + { + "filename": "dynamic-worker-allocation-in-pipelines-requires-balancing-utilization-against-queue-wait-time-because-high-utilization-causes-exponential-delay-growth.md", + "issues": [ + "missing_attribution_extractor" + ] + }, + { + "filename": "pipeline-bottleneck-formation-is-predictable-from-arrival-rate-and-service-time-distributions-enabling-proactive-capacity-planning.md", + "issues": [ + "missing_attribution_extractor" + ] + } + ], + "validation_stats": { + "total": 4, + "kept": 0, + "fixed": 4, + "rejected": 4, + "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-multi-server-queues-because-marginal-wait-time-reduction-decreases-as-server-count-increases.md:set_created:2026-03-15", + "dynamic-worker-allocation-in-pipelines-requires-balancing-utilization-against-queue-wait-time-because-high-utilization-causes-exponential-delay-growth.md:set_created:2026-03-15", + "pipeline-bottleneck-formation-is-predictable-from-arrival-rate-and-service-time-distributions-enabling-proactive-capacity-planning.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-multi-server-queues-because-marginal-wait-time-reduction-decreases-as-server-count-increases.md:missing_attribution_extractor", + "dynamic-worker-allocation-in-pipelines-requires-balancing-utilization-against-queue-wait-time-because-high-utilization-causes-exponential-delay-growth.md:missing_attribution_extractor", + "pipeline-bottleneck-formation-is-predictable-from-arrival-rate-and-service-time-distributions-enabling-proactive-capacity-planning.md:missing_attribution_extractor" + ] + }, + "model": "anthropic/claude-sonnet-4.5", + "date": "2026-03-15" +} \ No newline at end of file diff --git a/inbox/archive/2025-04-25-bournassenko-queueing-theory-cicd-pipelines.md b/inbox/archive/2025-04-25-bournassenko-queueing-theory-cicd-pipelines.md index d3d22c0c7..c46b46b83 100644 --- a/inbox/archive/2025-04-25-bournassenko-queueing-theory-cicd-pipelines.md +++ b/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", "square-root-staffing-principle-achieves-economies-of-scale-in-queueing-systems-by-operating-near-full-utilization-with-manageable-delays.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 published 'On Queueing Theory for Large-Scale CI/CD Pipelines Optimization' on arxiv (2504.18705) in April 2025 +- The paper models CI/CD pipeline stages as M/M/c queues with Poisson arrivals and exponential service times +- The framework integrates theoretical queueing analysis with practical optimization including dynamic scaling and prioritization