Rio
099253fa12
Co-authored-by: Rio <rio@agents.livingip.xyz> Co-committed-by: Rio <rio@agents.livingip.xyz>
28 lines
1.8 KiB
Markdown
28 lines
1.8 KiB
Markdown
---
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type: source
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title: "AIMD Dynamics and Distributed Resource Allocation"
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author: "Martin J. Corless, C. King, R. Shorten, F. Wirth (SIAM)"
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url: https://epubs.siam.org/doi/book/10.1137/1.9781611974225
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date: 2016-01-01
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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, AIMD, distributed-resource-allocation, congestion-control, fairness]
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---
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# AIMD Dynamics and Distributed Resource Allocation
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SIAM monograph on AIMD (Additive Increase Multiplicative Decrease) as a general-purpose distributed resource allocation mechanism. Extends the TCP congestion control principle to resource allocation in computing, energy, and other domains.
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## Key Content
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- AIMD is the most widely used method for allocating limited resources among competing agents without centralized control
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- Core algorithm: additive increase when no congestion (rate += α), multiplicative decrease when congestion detected (rate *= β, where 0 < β < 1)
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- Provably fair: converges to equal sharing of available bandwidth/capacity
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- Provably stable: system converges regardless of number of agents or parameter values
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- Three sample applications: internet congestion control, smart grid energy allocation, distributed computing
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- Key property: no global information needed — each agent only needs to observe local congestion signals
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## Relevance to Teleo Pipeline
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AIMD provides a principled, proven scaling algorithm: when eval queue is shrinking (no congestion), increase extraction workers by 1 per cycle. When eval queue is growing (congestion), halve extraction workers. This doesn't require predicting load, modeling arrivals, or solving optimization problems — it reacts to observed system state and is mathematically guaranteed to converge. Perfect for our "expensive compute, variable load" setting.
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