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Version: 2.7.0

Managing Quotas

Overview​

Quota scheduling is a sophisticated technique that enables effective management of request quotas. This technique empowers services to enforce rate limits for outbound or external API. This helps ensure that services stay within allocated rate limits, therefore avoiding penalties, ensuring smooth and uninterrupted operation.

Moreover, quota scheduling optimizes the utilization of request quotas by prioritizing access based on business-critical workloads. This strategic prioritization ensures that the most crucial requests receive their fair share of request quotas, aligning API usage with business objectives and preventing cost overages.

flowchart LR classDef TokenBucket fill:#F8773D,stroke:#000000,stroke-width:2px; classDef Signal fill:#EFEEED,stroke:#000000,stroke-width:1px; classDef Service fill:#56AE89,stroke:#000000,stroke-width:2px; classDef HP fill:#add8e6,stroke:#000000,stroke-width:1px; classDef LP fill:#ffb6c1,stroke:#000000,stroke-width:1px; BC("Bucket Capacity") --> TB RS("Fill Amount") --> TB TB[\Token Bucket/] class TB TokenBucket TB <-- "Counting" --> Agents subgraph " " Service -- "req/s" --> Agents class Service Service subgraph "Agents" subgraph "Scheduler" HP("High Priority - 200") class HP HP LP("Low Priority - 50") class LP LP end class Scheduler Service end class Agents MainClass HP --> API LP --> API class API Service end

The diagram provides an overview of quota scheduling in action, including the operation of the token bucket and its role in managing request admission. The token bucket, specified by a given bucket size and fill rate, performs counting and distributes tokens across agents.

Requests coming into the system are categorized into different workloads, each of which is defined by its priority and weight. This classification is crucial for the scheduling process within each agent.

Inside every agent, there is a scheduler that priorities request admission based on two factors: the priority and weight assigned to the corresponding workload, and the availability of tokens from the global token bucket. This mechanism ensures that high-priority requests are handled appropriately even under high load or when the request rate is close to the rate limit.

Example Scenario​

Consider the scenario of a cloud-based database service handling requests from several client applications with different priorities. By implementing a quota scheduling policy using Aperture, service operators can ensure fair and prioritized access. This policy enables prioritizing critical requests, preventing any single client application from monopolizing the database service or exhausting the available quota. Additionally, with Aperture's quota scheduling, the system becomes spend-aware, ensuring it stays within quota limits and avoids cost overages.