Quota Automation & Traffic Governance

Problem

Manual quota tuning could not scale with dynamic traffic growth across storage tenants. Incorrect quota settings risked either throttling legitimate traffic or allowing overload on storage backends.

My Role

Participated in the quota automation workflow, worked on metadata integration and enforcement path, and improved traffic governance reliability.

Architecture / Approach

Built a pipeline that predicts traffic based on historical metrics, generates automated quota recommendations with safety buffers and guardrails, and enforces quotas through the metadata control plane and a distributed rate limiter. Manual override and whitelist support preserved operational flexibility.

• Traffic prediction based on historical metrics
• Automated quota generation with safety buffer and guardrails
• Manual override and whitelist for operational flexibility
• Enforcement through metadata control plane and distributed rate limiter

Architecture Diagrams

flowchart LR
    Metrics[Traffic Metrics]
    Predict[Traffic Prediction]
    Quota[Quota Automation]
    Guard[Safety Rules / Guardrail]
    Manual[Whitelist / Override]
    Meta[Metadata Control Plane]
    Gateway[Storage Gateway API]
    RL[Distributed Rate Limiter]
    Metrics --> Predict
    Predict --> Quota
    Quota --> Guard
    Guard --> Meta
    Manual --> Meta
    Meta --> Gateway
    Gateway --> RL

Key Decisions

• Favored safe over-aggressive scaling to avoid throttling legitimate traffic
• Introduced safety guardrails to reduce prediction risk
• Preserved manual override for operational flexibility during incidents

Result

Reduced manual operational burden for quota management, improved quota rollout safety, and achieved better resilience against traffic spikes.

What I Learned

Learned that automation in capacity management needs both prediction intelligence and human override — fully automated systems without guardrails or manual escape hatches create operational risk.