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

type	title	author	url	date	domain	format	status	tags
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	unprocessed	pipeline-architecture operations-research markov-decision-process queueing-theory dynamic-programming

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.