t3.xlarge

Detailed performance metrics and analysis for Bifrost running on AWS t3.xlarge instances (4 vCPUs, 16GB RAM).

Instance Configuration

AWS t3.xlarge Specifications:

• vCPUs: 4
• Memory: 16GB RAM
• Network Performance: Up to 5 Gigabit

Bifrost Configuration:

• Buffer Size: 20,000
• Initial Pool Size: 15,000
• Test Load: 5,000 requests per second (RPS)

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Performance Results

Overall Performance Metrics

Metric	Value	Notes
Success Rate	100.00%	Perfect reliability under high load
Average Request Size	0.13 KB	Lightweight request payload
Average Response Size	10.32 KB	Large response payload testing
Average Latency	1.61s	Total end-to-end response time
Peak Memory Usage	3,340.44 MB	~21% of available 16GB RAM

Note: t3.xlarge tests used significantly larger response payloads (~10 KB vs ~1 KB on t3.medium) to stress-test performance with realistic production data sizes.

Detailed Performance Breakdown

Operation	Latency	Performance Notes
Queue Wait Time	1.67 µs	96% faster than t3.medium
Key Selection Time	10 ns	37% faster weighted API key selection
Message Formatting	2.11 µs	Consistent with t3.medium performance
Params Preparation	417 ns	Slight improvement over t3.medium
Request Body Preparation	2.36 µs	11% faster request assembly
JSON Marshaling	26.80 µs	58% faster serialization
Request Setup	7.17 µs	Comparable to t3.medium
HTTP Request	1.50s	4% faster provider API calls
Error Handling	162 ns	14% faster error processing
Response Parsing	2.11 ms	81% faster despite 7.5x larger payloads

Bifrost’s Total Overhead: 11 µs*

*Excludes JSON marshalling and HTTP calls, which are required in any implementation. 81% reduction compared to t3.medium (59 µs → 11 µs)

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Performance Analysis

Exceptional Performance Improvements

1. Dramatic Overhead Reduction: 81% lower Bifrost overhead (59 µs → 11 µs)
2. Superior Queue Management: 96% faster queue wait times (47.13 µs → 1.67 µs)
3. Faster JSON Processing: 58% improvement in marshaling despite larger payloads
4. Efficient Response Parsing: 81% faster parsing even with 7.5x larger responses
5. Perfect Reliability: 100% success rate maintained under high load

Resource Utilization

• Memory Efficiency: Uses only 21% of available RAM (3,340.44 MB / 16GB)
• CPU Performance: Excellent multi-core utilization for 5,000 RPS
• Headroom: Substantial capacity for traffic spikes and growth

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Scalability and Headroom

Exceptional Scaling Characteristics

The t3.xlarge configuration demonstrates excellent scaling potential:

Current Utilization:

• Memory: 21% used (13GB available headroom)
• Queue Performance: 1.67 µs wait time (near-optimal)
• Processing Speed: Sub-microsecond for most operations

Scaling Potential:

• Traffic Spikes: Can likely handle 15,000+ RPS bursts
• Response Size Growth: Efficiently handles 10 KB responses
• Concurrent Users: Supports thousands of simultaneous users

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Advanced Configuration

Optimal Settings for t3.xlarge

Based on test results, these configurations provide excellent performance:

{
  "client": {
    "initial_pool_size": 15000,
    "buffer_size": 20000
  }
}

Performance Tuning Opportunities

For Maximum Performance:

• Increase initial_pool_size to 18,000-20,000
• Increase buffer_size to 25,000-30,000
• Trade-off: Higher memory usage (still well within limits)

For Memory Optimization:

• Current config already very efficient at 21% RAM usage
• Could reduce settings if needed, but performance gains would be lost

For Extreme Workloads:

• Consider initial_pool_size up to 25,000
• Increase buffer_size to 35,000+
• Monitor memory usage approaching 50% of available RAM

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Performance Comparison

vs. t3.medium Performance

Metric	t3.medium	t3.xlarge	Improvement
Bifrost Overhead	59 µs	11 µs	-81%
Average Latency	2.12s	1.61s	-24%
Queue Wait Time	47.13 µs	1.67 µs	-96%
JSON Marshaling	63.47 µs	26.80 µs	-58%
Response Parsing	11.30 ms	2.11 ms	-81%
Response Size Handled	1.37 KB	10.32 KB	+7.5x
Peak Memory Usage	1,312.79 MB	3,340.44 MB	+155%
Memory Utilization	33%	21%	-36%

Key Insights:

• 81% overhead reduction while handling 7.5x larger responses
• Exceptional efficiency with only 21% memory utilization
• Dramatic queue performance improvements
• Substantial headroom for growth and traffic spikes

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Next Steps

• Run Your Own Benchmarks with your specific payload sizes
• Compare with t3.medium for cost-optimization analysis