Rio
099253fa12
Co-authored-by: Rio <rio@agents.livingip.xyz> Co-committed-by: Rio <rio@agents.livingip.xyz>
29 lines
1.7 KiB
Markdown
29 lines
1.7 KiB
Markdown
---
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type: source
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title: "An Overview for Markov Decision Processes in Queues and Networks"
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author: "Quan-Lin Li, Jing-Yu Ma, Rui-Na Fan, Li Xia"
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url: https://arxiv.org/abs/1907.10243
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date: 2019-07-24
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domain: internet-finance
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format: paper
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status: unprocessed
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tags: [pipeline-architecture, operations-research, markov-decision-process, queueing-theory, dynamic-programming]
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---
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# An Overview for Markov Decision Processes in Queues and Networks
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Comprehensive 42-page survey of MDP applications in queueing systems, covering 60+ years of research from the 1960s to present.
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## Key Content
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- Continuous-time MDPs for queue management: decisions happen at state transitions (arrivals, departures)
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- Classic results: optimal policies often have threshold structure — "serve if queue > K, idle if queue < K"
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- For multi-server systems: optimal admission and routing policies are often simple (join-shortest-queue, threshold-based)
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- Dynamic programming and stochastic optimization provide tools for deriving optimal policies
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- Key challenge: curse of dimensionality — state space explodes with multiple queues/stages
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- Practical approaches: approximate dynamic programming, reinforcement learning for large state spaces
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- Emerging direction: deep RL for queue management in networks and cloud computing
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## Relevance to Teleo Pipeline
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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.
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