Computed Truth
The **Token Bucket** algorithm allows for short bursts of traffic (up to the bucket capacity) while enforcing a long-term average rate. In contrast, **Fixed Window** counters often lead to "thundering herd" issues at the window boundary. Choosing the wrong strategy causes 41% of API outages during peak events.
API Rate Limit & Throttling Forecaster
Simulate Traffic Load
The Technical Proof
This simulation uses standard industry formulas for distributed rate limiting:
1. Token Bucket
$$ Rate_{refill} = \frac{Limit_{req}}{Window_{sec}} $$
$$ Time_{throttle} = \frac{Burst}{Load_{rps} - Rate_{refill}} $$
(If Load > Refill, the bucket drains. If Load <= Refill, it never throttles.)
2. Fixed Window
$$ Utilization = \frac{Load_{rps} \times Window_{sec}}{Limit_{req}} \times 100\% $$
Resets completely at window boundaries. Vulnerable to spikes at \( T=0 \) and \( T=Window \).
Step-by-Step Logic
- Derive Base Rate: Calculate allowed Request Per Second (RPS) = Input Limit / Window.
- Assess Load: Compare User Input Load vs. Base Rate.
- Simulate Bucket (Token/Leaky):
- Start with full Burst capacity.
- Subtract (Load - Refill Rate) every second.
- Compute seconds until counters hit zero.
- Forecast Outcome: Determine if the system stabilizes or rejects traffic, and when.