bf4858d0f7
- What: operations research, queueing theory, stochastic modeling for pipeline architecture - Why: Leo/Cory brief — need disciplined approach to variable-load scaling Pentagon-Agent: Rio <2EA8DBCB-A29B-43E8-B726-45E571A1F3C8>
30 lines
1.9 KiB
Markdown
30 lines
1.9 KiB
Markdown
---
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type: source
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title: "Optimal Control Policies for Resource Allocation in the Cloud: Comparison Between Markov Decision Process and Heuristic Approaches"
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author: "Thomas Tournaire, Hind Castel-Taleb, Emmanuel Hyon"
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url: https://arxiv.org/abs/2104.14879
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date: 2021-04-30
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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, cloud-autoscaling, optimal-control]
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---
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# Optimal Control Policies for Resource Allocation in the Cloud
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Compares MDP-based optimal scaling policies against heuristic approaches for cloud auto-scaling. The MDP formulation treats VM provisioning as a sequential decision problem.
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## Key Content
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- Auto-scaling problem: VMs turned on/off based on queue occupation to minimize combined energy + performance cost
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- MDP formulation: states = queue lengths + active VMs, actions = add/remove VMs, rewards = negative cost (energy + SLA violations)
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- Value iteration and policy iteration algorithms find optimal threshold policies
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- Structured MDP algorithms incorporating hysteresis properties outperform heuristics in both execution time and accuracy
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- Hysteresis: different thresholds for scaling up vs. scaling down — prevents oscillation (e.g., scale up at queue=10, scale down at queue=3)
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- MDP algorithms find optimal hysteresis thresholds automatically
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## Relevance to Teleo Pipeline
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The MDP formulation maps directly: states = (unprocessed queue, in-flight extractions, open PRs, active workers), actions = (spawn worker, kill worker, wait), cost = (Claude compute cost per worker-minute + delay cost per queued source). The hysteresis insight is particularly valuable — we should have different thresholds for spinning up vs. spinning down workers to prevent oscillation.
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Key finding: structured MDP with hysteresis outperforms simple threshold heuristics. But even simple threshold policies (scale up at queue=N, scale down at queue=M where M < N) perform reasonably well.
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