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42 lines
3.2 KiB
Markdown
42 lines
3.2 KiB
Markdown
---
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type: source
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title: "AIMD Scheduling and Resource Allocation in Distributed Computing Systems"
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author: "Vlahakis, Athanasopoulos et al."
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url: https://arxiv.org/abs/2109.02589
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date: 2021-09-06
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domain: internet-finance
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format: paper
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status: processed
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tags: [pipeline-architecture, operations-research, AIMD, distributed-computing, resource-allocation, congestion-control]
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processed_by: rio
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processed_date: 2026-03-11
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claims_extracted: ["aimd-congestion-control-generalizes-to-distributed-resource-allocation-because-queue-dynamics-are-structurally-identical-across-networks-and-compute-pipelines.md", "aimd-worker-scaling-requires-only-queue-state-observation-not-load-prediction-making-it-simpler-than-ml-based-autoscaling.md"]
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extraction_model: "anthropic/claude-sonnet-4.5"
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extraction_notes: "Extracted two mechanism design claims about AIMD's generalization from network congestion control to distributed computing autoscaling. The source is a 2021 academic paper proving mathematical properties of AIMD in multi-queue distributed systems. Primary relevance is to pipeline architecture and operations research, with direct application to Teleo's extract-eval pipeline scaling problem. No entities to create (academic paper, no companies/products/decisions). No enrichments identified — these are novel mechanism insights not covered by existing claims in the KB."
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---
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# AIMD Scheduling and Resource Allocation in Distributed Computing Systems
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Applies TCP's AIMD (Additive Increase Multiplicative Decrease) congestion control to distributed computing resource allocation — scheduling incoming requests across computing nodes.
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## Key Content
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- Models distributed system as multi-queue scheme with computing nodes
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- Proposes AIMD-like admission control: stable irrespective of total node count and AIMD parameters
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- Key insight: congestion control in networks and worker scaling in compute pipelines are the same problem — matching producer rate to consumer capacity
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- Decentralized resource allocation using nonlinear state feedback achieves global convergence to bounded set in finite time
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- Connects to QoS via Little's Law: local queuing time calculable from simple formula
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- AIMD is proven optimal for fair allocation of shared resources among competing agents without centralized control
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## Relevance to Teleo Pipeline
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AIMD provides an elegant scaling policy: when queue is shrinking (system healthy), add workers linearly (e.g., +1 per cycle). When queue is growing (system overloaded), cut workers multiplicatively (e.g., halve them). This is self-correcting, proven stable, and doesn't require predicting load — it reacts to observed queue state.
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The TCP analogy is precise: our pipeline "bandwidth" is eval throughput. When extract produces faster than eval can consume, we need backpressure (slow extraction) or scale-up (more eval workers). AIMD handles this naturally.
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## Key Facts
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- AIMD (Additive Increase Multiplicative Decrease) is TCP's congestion control algorithm
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- Vlahakis et al. (2021) proved AIMD stability for distributed computing resource allocation
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- AIMD achieves global convergence to bounded set in finite time regardless of node count
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- Little's Law connects queue length to QoS metrics in AIMD systems
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