---
type: source
title: "AIMD Scheduling and Resource Allocation in Distributed Computing Systems"
author: "Vlahakis, Athanasopoulos et al."
url: https://arxiv.org/abs/2109.02589
date: 2021-09-06
domain: internet-finance
format: paper
status: processed
tags: [pipeline-architecture, operations-research, AIMD, distributed-computing, resource-allocation, congestion-control]
processed_by: rio
processed_date: 2026-03-11
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"]
extraction_model: "anthropic/claude-sonnet-4.5"
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."
---

# AIMD Scheduling and Resource Allocation in Distributed Computing Systems

Applies TCP's AIMD (Additive Increase Multiplicative Decrease) congestion control to distributed computing resource allocation — scheduling incoming requests across computing nodes.

## Key Content

- Models distributed system as multi-queue scheme with computing nodes
- Proposes AIMD-like admission control: stable irrespective of total node count and AIMD parameters
- Key insight: congestion control in networks and worker scaling in compute pipelines are the same problem — matching producer rate to consumer capacity
- Decentralized resource allocation using nonlinear state feedback achieves global convergence to bounded set in finite time
- Connects to QoS via Little's Law: local queuing time calculable from simple formula
- AIMD is proven optimal for fair allocation of shared resources among competing agents without centralized control

## Relevance to Teleo Pipeline

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.

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.


## Key Facts
- AIMD (Additive Increase Multiplicative Decrease) is TCP's congestion control algorithm
- Vlahakis et al. (2021) proved AIMD stability for distributed computing resource allocation
- AIMD achieves global convergence to bounded set in finite time regardless of node count
- Little's Law connects queue length to QoS metrics in AIMD systems