Asynchronous Statistic-Based Rate Limiting for Uneven Distributed Loads
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Solution Overview
Problem
Existing rate limiting methods in distributed systems suffer from network latency and inefficiencies, particularly when centralized solutions are used, and local rate limiters fail to adapt to uneven request distributions across instances.
Innovation Solution
Implementing local rate limiters that measure and aggregate throughput statistics asynchronously, allowing each instance to adjust its limits based on overall system capacity and instance-specific usage, without requiring real-time network communications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a centralized rate limiter is used to manage requests across all instances, then service availability is protected from excessive use, but network bandwidth is consumed and performance is slowed due to required calls before each request
Solution Approach 1:
The centralized rate limiter is segmented into distributed local rate limiters at each instance. Each instance independently tracks its own request count against its allocated capacity share, eliminating the need for network calls before each request while maintaining overall rate limiting effectiveness.
Solution Approach 2:
Each instance's local rate limiter serves itself by autonomously determining whether to allow or reject requests based on its locally stored capacity allocation and request count, without requiring external validation from a central authority for each individual request.
2Productivity
If local rate limiters are assigned equal shares of overall capacity, then network calls are eliminated and performance improves, but the system cannot adapt when request distribution becomes uneven across instances
Solution Approach 1:
A feedback mechanism collects actual throughput statistics from each instance and reports them to a central coordinator. The coordinator recalculates capacity allocations based on observed usage patterns and distributes updated allocations to instances, enabling adaptation to uneven load distributions while maintaining high throughput.
Solution Approach 2:
The capacity allocation for each instance is made dynamic rather than static. Allocations are periodically adjusted based on observed throughput statistics and current system conditions, allowing the system to adapt to changing load distributions while instances continue to operate with local decision-making.
3Reliability
If a centralized cache of requests is used for rate limiting, then rate limits can be enforced across all instances, but network calls are required before each request which slows performance
Solution Approach 1:
The rate limiting function is extracted from the centralized cache and embedded locally at each instance. Each instance maintains its own request count and capacity allocation locally, eliminating the need for network calls to a centralized cache while preserving rate limit enforcement through local decision-making.
4Device complexity
If local rate limiters enforce strict capacity limits, then resource allocation is controlled, but instances with lower actual usage are unnecessarily throttled when other instances generate more requests
Solution Approach 1:
Throughput statistics from all instances are collected and fed back to the capacity allocation system. When some instances have lower usage than their allocated capacity, the system detects this surplus and reallocates capacity to instances with higher demand, ensuring optimal utilization across the system while maintaining local control.
Data Source
AI summary
In an example embodiment, rate limiting is performed at the instance level (i.e., locally), but utilizing throughput statistics of other instances. These statistics may be measured locally by each instance and then transmitted to a central store, where they are aggregated. Each instance is then able to asynchronously request the aggregated statistics from the central store and use this information to manage the parameters of its own local rate limiter.