This repository contains architecture notes exploring reliability mechanisms used in latency-sensitive distributed systems.
The focus is on platform patterns used in large-scale systems such as:
- AI inference platforms
- API gateways
- event-driven platforms
- multi-tenant SaaS infrastructure
These notes explore how systems remain stable under burst traffic, multi-tenant workloads, and unpredictable request cost.
Reliable distributed platforms typically rely on three fundamental control mechanisms:
- Admission Control
- Fairness Scheduling
- Bounded Queues and Overload Protection
Together these mechanisms protect latency SLOs and prevent resource collapse under load.
Admission control protects the system by preventing requests that cannot meet latency objectives from entering the platform.
Document: Admission Control
Topics covered:
- queue-induced latency collapse
- deadline-aware admission
- latency SLO protection
- inference gateway design
Multi-tenant systems must ensure that high-volume tenants cannot monopolize system capacity.
Document: Fairness Scheduling
Topics covered:
- noisy neighbor problem
- Deficit Round Robin
- Weighted Fair Queuing
- fairness vs performance tradeoffs
Queues must be bounded to prevent hidden latency growth and cascading failures.
Document: Bounded Queues and Overload Control
Topics covered:
- queue-induced latency collapse
- load shedding
- bufferbloat
- overload protection strategies
A typical latency-sensitive platform architecture combines these mechanisms.
Client
│
▼
Gateway
(admission control)
│
▼
Scheduler
(fairness control)
│
▼
Bounded Queue
│
▼
Workers
Each layer protects the system in a different way:
Admission control
prevents overload before requests enter the system.
Fairness scheduling
ensures tenants share resources predictably.
Bounded queues
protect latency and prevent runaway backlog growth.
These patterns are commonly used in large-scale inference systems where:
- request cost varies significantly
- GPU resources are limited
- latency requirements are strict
The architecture explored in the AI Inference Platform Lab demonstrates these mechanisms in practice.
Repository:
https://github.com/CeciliaGit/ai-inference-platform-lab
These notes are intended as a concise reference for platform architects and engineers designing systems that must remain stable under unpredictable demand.
The emphasis is on architecture principles and system behavior, not implementation details.
- Glossary
- Admission Control
- Fairness Scheduling
- Bounded Queues and Overload Control
- Multi-Region Distributed Systems