2.3 KiB
2.3 KiB
| type | title | author | url | date | domain | format | status | tags | processed_by | processed_date | claims_extracted | extraction_model | extraction_notes | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| source | Resource Scheduling in Non-Stationary Service Systems | Simio / WinterSim 2018 | https://www.simio.com/resources/papers/WinterSim2018/Resource-Scheduling-In-Non-stationary-Service-Systems.php | 2018-12-01 | internet-finance | paper | processed |
|
rio | 2026-03-11 |
|
anthropic/claude-sonnet-4.5 | Single claim extracted validating dynamic worker allocation for pipeline architecture. Paper provides theoretical foundation for queue-depth-based scaling vs fixed worker pools. No entity data (academic paper, no companies/products/people to track). No enrichments to existing claims (this is infrastructure theory, not market mechanism design). |
Resource Scheduling in Non-Stationary Service Systems
WinterSim 2018 paper on scheduling resources (servers/workers) when arrival rates change over time. Addresses the gap between theoretical queueing models (which assume stationarity) and real systems (which don't).
Key Content
- Non-stationary service systems require time-varying staffing — fixed worker counts are suboptimal
- The goal: determine the number of servers as a function of time
- Without server constraints there would be no waiting time, but this wastes capacity since arrivals are stochastic and nonstationary
- Simulation-based approach: use discrete-event simulation to test staffing policies against realistic arrival patterns
- Key tradeoff: responsiveness (adding workers fast when load spikes) vs. efficiency (not wasting workers during quiet periods)
Relevance to Teleo Pipeline
Directly applicable: our pipeline needs time-varying worker counts, not fixed MAX_WORKERS. The paper validates the approach of measuring queue depth and adjusting workers dynamically rather than using static cron-based fixed pools.
Key Facts
- WinterSim 2018 conference paper on resource scheduling
- Addresses queueing theory gap between stationary assumptions and nonstationary reality
- Proposes simulation-based staffing optimization for time-varying demand