HTTP underpins modern Internet services, and providers enforce quotas to regulate HTTP API traffic for scalability and reliability. When requests exceed quotas, clients are throttled and must retry. Server-side enforcement protects the service. However, when independent clients' usage counts toward a shared quota, server-only controls are inefficient; clients lack visibility into others' load, causing their retry attempts to potentially fail. Indeed, retry timing is important since each attempt incurs costs and yields no benefit unless admitted. While centralized coordination could address this, practical limitations have led to widespread adoption of simple client-side strategies like exponential backoff. As we show, these simple strategies cause excessive retries and significant costs. We design adaptive client-side mechanisms requiring no central control, relying only on minimal feedback. We present two algorithms: ATB, an offline method deployable via service workers, and AATB, which enhances retry behavior using aggregated telemetry data. Both algorithms infer system congestion to schedule retries. Through emulations with real-world traces and synthetic datasets with up to 100 clients, we demonstrate that our algorithms reduce HTTP 429 errors by up to 97.3% compared to exponential backoff, while the modest increase in completion time is outweighed by the reduction in errors.

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