teleo-codex/inbox/archive/2021-09-00-vlahakis-aimd-scheduling-distributed-computing.md

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