extract: 2016-00-00-cambridge-staffing-non-poisson-non-stationary-arrivals
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---
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type: claim
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domain: internet-finance
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description: "Bursty arrival processes require more safety capacity than Poisson models predict, scaled by variance-to-mean ratio"
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confidence: proven
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source: "Whitt et al., 'Staffing a Service System with Non-Poisson Non-Stationary Arrivals', Cambridge Core, 2016"
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created: 2026-03-11
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# Square-root staffing formula requires peakedness adjustment for non-Poisson arrivals because bursty processes need proportionally more safety capacity than the Poisson baseline predicts
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The standard square-root staffing formula (workers = mean load + safety factor × √mean) assumes Poisson arrivals where variance equals mean. Real-world arrival processes violate this assumption through burstiness (arrivals clustered in time) or smoothness (arrivals more evenly distributed than random).
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Whitt et al. extend the square-root staffing rule by introducing **peakedness** — the variance-to-mean ratio of the arrival process — as the key adjustment parameter. For bursty arrivals (peakedness > 1), systems require MORE safety capacity than Poisson models suggest. For smooth arrivals (peakedness < 1), systems need LESS.
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The modified staffing formula adjusts the square-root safety margin by multiplying by the square root of peakedness. This correction is critical for non-stationary systems where arrival rates vary over time (daily cycles, seasonal patterns, or event-driven spikes).
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## Evidence
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- Whitt et al. (2016) prove that peakedness — the variance-to-mean ratio — captures the essential non-Poisson behavior for staffing calculations
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- Standard Poisson assumption (variance = mean) fails empirically for bursty workloads like research paper dumps, product launches, or customer service spikes
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- Using constant staffing (fixed MAX_WORKERS) regardless of queue state creates dual failure: over-provisioning during quiet periods (wasted compute) and under-provisioning during bursts (queue explosion)
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## Relevance to Pipeline Architecture
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Teleo's research pipeline exhibits textbook non-Poisson non-stationary arrivals: research dumps arrive in bursts of 15+ sources, futardio launches come in waves of 20+ proposals, while other days see minimal activity. The peakedness parameter quantifies exactly how much extra capacity is needed beyond naive square-root staffing.
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This directly informs dynamic worker scaling: measure empirical peakedness from historical arrival data, adjust safety capacity accordingly, and scale workers based on current queue depth rather than using fixed limits.
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---
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Relevant Notes:
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- domains/internet-finance/_map
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Topics:
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- core/mechanisms/_map
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---
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type: claim
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domain: internet-finance
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description: "Replacing non-stationary arrival rates with constant staffing leads to systematic over- or under-provisioning"
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confidence: proven
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source: "Whitt et al., 'Staffing a Service System with Non-Poisson Non-Stationary Arrivals', Cambridge Core, 2016"
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created: 2026-03-11
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---
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# Time-varying arrival rates require dynamic staffing not constant MAX_WORKERS because using average or maximum rates as constants creates systematic misallocation across the arrival cycle
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Non-stationary arrival processes — where the arrival rate itself changes over time — cannot be efficiently staffed with constant worker counts. Whitt et al. demonstrate that replacing time-varying rates with either the average rate or the maximum rate produces badly mis-staffed systems:
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- **Constant = average rate**: Under-staffed during peak periods, leading to queue explosions and service degradation
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- **Constant = maximum rate**: Over-staffed during off-peak periods, wasting capacity and compute resources
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The optimal approach tracks the arrival rate over time and adjusts staffing dynamically to match the current load plus an appropriate safety margin (scaled by peakedness for non-Poisson processes).
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## Evidence
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- Whitt et al. (2016) prove that time-varying arrival rates require time-varying staffing levels for efficiency
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- Constant staffing at maximum capacity wastes resources during low-traffic periods
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- Constant staffing at average capacity fails catastrophically during burst periods
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- Dynamic staffing based on current queue state and arrival rate estimates achieves both efficiency (no waste during quiet periods) and reliability (adequate capacity during bursts)
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## Application to Teleo Pipeline
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Teleo's research processing pipeline exhibits strong non-stationarity: research dumps and futardio launches create burst periods with 15-20+ simultaneous arrivals, while other periods see minimal activity. Using a fixed MAX_WORKERS setting (constant staffing) is the worst of both worlds:
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- During bursts: MAX_WORKERS is too low, queue explodes, processing stalls
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- During quiet periods: MAX_WORKERS is too high, workers sit idle, compute wasted
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Dynamic worker scaling based on current queue depth and estimated arrival rate (with peakedness adjustment) is the theoretically correct solution.
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---
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Relevant Notes:
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- [[square-root-staffing-formula-requires-peakedness-adjustment-for-non-poisson-arrivals]]
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- domains/internet-finance/_map
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Topics:
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- core/mechanisms/_map
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@ -6,8 +6,13 @@ url: https://www.cambridge.org/core/journals/probability-in-the-engineering-and-
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date: 2016-01-01
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domain: internet-finance
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format: paper
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status: unprocessed
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status: processed
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tags: [pipeline-architecture, operations-research, stochastic-modeling, non-stationary-arrivals, capacity-sizing]
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processed_by: rio
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processed_date: 2026-03-11
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claims_extracted: ["square-root-staffing-formula-requires-peakedness-adjustment-for-non-poisson-arrivals.md", "time-varying-arrival-rates-require-dynamic-staffing-not-constant-max-workers.md"]
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extraction_model: "anthropic/claude-sonnet-4.5"
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extraction_notes: "Operations research paper on staffing under non-Poisson non-stationary arrivals. Extracted two claims on peakedness adjustment and dynamic staffing requirements. Direct application to Teleo pipeline architecture for worker scaling. No entity data (academic paper, no companies/products/decisions). No enrichments (novel theoretical contribution not covered by existing claims)."
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---
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# Staffing a Service System with Non-Poisson Non-Stationary Arrivals
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