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

type	title	author	url	date	domain	format	status	tags
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	unprocessed	pipeline-architecture operations-research AIMD distributed-computing resource-allocation congestion-control

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.