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..9df954d83 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 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: 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..0c12f979e 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. 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: 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..25e4d1a8b 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 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: 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..a3a00dcaf --- /dev/null +++ b/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" +} \ 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..1b7f174b5 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"] +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