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

type	title	author	url	date	domain	format	status	tags	processed_by	processed_date	enrichments_applied	extraction_model
source	On Queueing Theory for Large-Scale CI/CD Pipelines Optimization	Grégory Bournassenko	https://arxiv.org/abs/2504.18705	2025-04-25	internet-finance	paper	enrichment	pipeline-architecture operations-research queueing-theory ci-cd M/M/c-queue	rio	2026-03-16	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	anthropic/claude-sonnet-4.5

On Queueing Theory for Large-Scale CI/CD Pipelines Optimization

Academic paper applying classical M/M/c queueing theory to model CI/CD pipeline systems. Proposes a queueing theory modeling framework to optimize large-scale build/test workflows using multi-server queue models.

Key Content

• Addresses bottleneck formation in high-volume shared infrastructure pipelines
• Models pipeline stages as M/M/c queues (Poisson arrivals, exponential service, c servers)
• Integrates theoretical queueing analysis with practical optimization — dynamic scaling and prioritization of CI/CD tasks
• Framework connects arrival rate modeling to worker count optimization
• Demonstrates that classical queueing models provide actionable guidance for real software pipelines

Relevance to Teleo 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

• M/M/c queues model Poisson arrivals, exponential service times, and c servers
• Classical queueing theory provides closed-form solutions for expected wait times in multi-server systems
• The paper addresses bottleneck formation in high-volume shared infrastructure pipelines
• Framework integrates theoretical queueing analysis with practical optimization for dynamic scaling