teleo-codex/inbox/archive/2019-07-00-li-overview-mdp-queues-networks.md

type	title	author	url	date	domain	format	status	tags	processed_by	processed_date	claims_extracted	extraction_model	extraction_notes
source	An Overview for Markov Decision Processes in Queues and Networks	Quan-Lin Li, Jing-Yu Ma, Rui-Na Fan, Li Xia	https://arxiv.org/abs/1907.10243	2019-07-24	internet-finance	paper	processed	pipeline-architecture operations-research markov-decision-process queueing-theory dynamic-programming	rio	2026-03-11	optimal-queue-policies-have-threshold-structure-making-simple-rules-near-optimal.md pipeline-state-space-size-determines-whether-exact-mdp-solution-or-threshold-heuristics-are-optimal.md	anthropic/claude-sonnet-4.5	Academic survey of MDP applications to queueing theory. Extracted two claims about optimal policy structure and state space tractability. No entities (academic paper, no companies/products). No enrichments (claims are foundational operations research results, not directly connected to existing futarchy/capital formation claims in KB).

An Overview for Markov Decision Processes in Queues and Networks

Comprehensive 42-page survey of MDP applications in queueing systems, covering 60+ years of research from the 1960s to present.

Key Content

• Continuous-time MDPs for queue management: decisions happen at state transitions (arrivals, departures)
• Classic results: optimal policies often have threshold structure — "serve if queue > K, idle if queue < K"
• For multi-server systems: optimal admission and routing policies are often simple (join-shortest-queue, threshold-based)
• Dynamic programming and stochastic optimization provide tools for deriving optimal policies
• Key challenge: curse of dimensionality — state space explodes with multiple queues/stages
• Practical approaches: approximate dynamic programming, reinforcement learning for large state spaces
• Emerging direction: deep RL for queue management in networks and cloud computing

Relevance to Teleo Pipeline

Our pipeline has a manageable state space (queue depths across 3 stages, worker counts, time-of-day) — small enough for exact MDP solution via value iteration. The survey confirms that optimal policies for our type of system typically have threshold structure: "if queue > X and workers < Y, spawn a worker." This means even without solving the full MDP, a well-tuned threshold policy will be near-optimal.

Key Facts

• Li et al. survey covers 60+ years of MDP research in queueing systems (1960s-2019)
• Continuous-time MDPs for queues: decisions happen at state transitions (arrivals, departures)
• Classic optimal policies: threshold structure (serve if queue > K, idle if queue < K)
• Multi-server optimal policies: join-shortest-queue, threshold-based admission
• Key challenge: curse of dimensionality with multiple queues/stages
• Practical approaches: approximate dynamic programming, reinforcement learning for large state spaces
• Emerging direction: deep RL for queue management in networks and cloud computing