teleo-codex/inbox/archive/2019-00-00-liu-modeling-nonstationary-non-poisson-arrival-processes.md
Teleo Agents bf4858d0f7 rio: research pipeline scaling disciplines — 15 sources archived
- What: operations research, queueing theory, stochastic modeling for pipeline architecture
- Why: Leo/Cory brief — need disciplined approach to variable-load scaling

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type title author url date domain format status tags
source Modeling and Simulation of Nonstationary Non-Poisson Arrival Processes Yunan Liu et al. (NC State) https://yunanliu.wordpress.ncsu.edu/files/2019/11/CIATApublished.pdf 2019-01-01 internet-finance paper unprocessed
pipeline-architecture
stochastic-modeling
non-stationary-arrivals
MMPP
batch-arrivals

Modeling and Simulation of Nonstationary Non-Poisson Arrival Processes

Introduces the CIATA (Combined Inversion-and-Thinning Approach) method for modeling nonstationary non-Poisson processes characterized by a rate function, mean-value function, and asymptotic variance-to-mean (dispersion) ratio.

Key Content

  • Standard Poisson process assumptions break down when arrivals are bursty or correlated
  • CIATA models target arrival processes via rate function + dispersion ratio — captures both time-varying intensity and burstiness
  • The Markov-MECO process (a Markovian arrival process / MAP) models interarrival times as absorption times of a continuous-time Markov chain
  • Markov-Modulated Poisson Process (MMPP): arrival rate switches between states governed by a hidden Markov chain — natural model for "bursty then quiet" patterns
  • Key finding: replacing a time-varying arrival rate with a constant (max or average) leads to systems being badly understaffed or overstaffed
  • Congestion measures are increasing functions of arrival process variability — more bursty = more capacity needed

Relevance to Teleo Pipeline

Our arrival process is textbook MMPP: there's a hidden state (research session happening vs. quiet period) that governs the arrival rate. During research sessions, sources arrive in bursts of 10-20. During quiet periods, maybe 0-2 per day. The MMPP framework models this directly and gives us tools to size capacity for the mixture of states rather than the average.