TutorialsLoad Patterns & Scheduling

Fixed Schedule Benchmarking

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Fixed schedule benchmarking provides precise timing control by executing requests at specific timestamps. This mode is ideal for simulating exact traffic patterns, testing temporal performance characteristics, and reproducing time-sensitive scenarios.

Overview

Fixed schedule mode enables:

  • Precise Timing: Execute requests at exact millisecond intervals
  • Traffic Simulation: Replicate real-world traffic patterns
  • Performance Analysis: Identify how response times vary with request timing
  • Load Testing: Test system behavior under controlled temporal stress patterns

Fixed Schedule File Format

Fixed schedule files use JSONL format with timestamp-based entries:

1{"timestamp": 0, "input_length": 100, "output_length": 200, "hash_ids": [1001]}
2{"timestamp": 500, "input_length": 200, "output_length": 400, "hash_ids": [1002]}
3{"timestamp": 1000, "input_length": 550, "output_length": 500, "hash_ids": [1003, 1005]}

Field Descriptions:

  • timestamp: Milliseconds from schedule start when request should be sent
  • input_length: Number of tokens in the input prompt
  • input_text: Exact text to send in the request (provided instead of input_length)
  • output_length: Maximum number of tokens in the response (optional)
  • hash_ids: Hash block identifiers to simulate text reuse with 512-token blocks (optional)

Basic Fixed Schedule Execution

Setting Up the Server

$# Start vLLM server for fixed schedule testing
$docker pull vllm/vllm-openai:latest
$docker run --gpus all -p 8000:8000 vllm/vllm-openai:latest \
>  --model Qwen/Qwen3-0.6B \
>  --host 0.0.0.0 --port 8000 &
$# Wait for server to be ready
$timeout 900 bash -c 'while [ "$(curl -s -o /dev/null -w "%{http_code}" localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d "{\"model\":\"Qwen/Qwen3-0.6B\",\"messages\":[{\"role\":\"user\",\"content\":\"test\"}],\"max_tokens\":1}")" != "200" ]; do sleep 2; done' || { echo "vLLM not ready after 15min"; exit 1; }

Running Basic Fixed Schedule

$# Create a fixed schedule with precise timing
$cat > precise_schedule.jsonl << 'EOF'
${"timestamp": 0, "input_length": 100, "hash_ids": [3001]}
${"timestamp": 500, "input_length": 200, "hash_ids": [3002]}
${"timestamp": 750, "input_length": 150, "hash_ids": [3003]}
${"timestamp": 1000, "input_length": 300, "hash_ids": [3004]}
${"timestamp": 1250, "input_length": 180, "hash_ids": [3005]}
${"timestamp": 2000, "input_length": 400, "hash_ids": [3006]}
${"timestamp": 2500, "input_length": 250, "hash_ids": [3007]}
${"timestamp": 3000, "input_length": 350, "hash_ids": [3008]}
${"timestamp": 4000, "input_length": 500, "hash_ids": [3009]}
${"timestamp": 5000, "input_length": 600, "hash_ids": [3010, 3050]}
$EOF
$# Run basic fixed schedule benchmarking
$aiperf profile \
>    --model Qwen/Qwen3-0.6B \
>    --endpoint-type chat \
>    --endpoint /v1/chat/completions \
>    --streaming \
>    --url localhost:8000 \
>    --input-file precise_schedule.jsonl \
>    --custom-dataset-type mooncake_trace \
>    --fixed-schedule \
>    --fixed-schedule-auto-offset

Sample Output (Successful Run):

INFO     Starting AIPerf System
INFO     Using Fixed Schedule mode with auto-offset
INFO     Loaded 10 entries from precise_schedule.jsonl
INFO     Schedule duration: 5.0 seconds
INFO     AIPerf System is PROFILING
Profiling: 10/10 |████████████████████████| 100% [00:05<00:00]
INFO     Benchmark completed successfully
INFO     Results saved to: artifacts/Qwen_Qwen3-0.6B-chat-fixed-schedule/
            NVIDIA AIPerf | LLM Metrics
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┓
┃                     Metric ┃    avg ┃    min ┃    max ┃    p99 ┃    p50 ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━┩
│       Request Latency (ms) │ 345.67 │ 234.56 │ 498.12 │ 476.34 │ 338.90 │
│   Time to First Token (ms) │  78.45 │  52.34 │ 112.67 │ 108.23 │  76.12 │
│   Inter Token Latency (ms) │  15.23 │  11.45 │  22.34 │  21.12 │  14.89 │
│ Request Throughput (req/s) │   2.89 │      - │      - │      - │      - │
└────────────────────────────┴────────┴────────┴────────┴────────┴────────┘
JSON Export: artifacts/Qwen_Qwen3-0.6B-chat-fixed-schedule/profile_export_aiperf.json

Key Parameters:

  • --fixed-schedule-auto-offset: Automatically adjusts timestamps to start from 0

Advanced Schedule Patterns

Time Window Execution

Execute only a portion of the schedule using start and end offsets:

$# Execute schedule from 2s to 6s window
$aiperf profile \
>    --model Qwen/Qwen3-0.6B \
>    --endpoint-type chat \
>    --endpoint /v1/chat/completions \
>    --streaming \
>    --url localhost:8000 \
>    --input-file precise_schedule.jsonl \
>    --custom-dataset-type mooncake_trace \
>    --fixed-schedule \
>    --fixed-schedule-start-offset 2000 \
>    --fixed-schedule-end-offset 4000

Sample Output (Successful Run):

INFO     Starting AIPerf System
INFO     Using Fixed Schedule mode with time window [2000ms - 4000ms]
INFO     Loaded 10 entries from precise_schedule.jsonl
INFO     Filtered to 2 entries within time window
INFO     Schedule duration: 2.0 seconds
INFO     AIPerf System is PROFILING
Profiling: 2/2 |████████████████████████| 100% [00:02<00:00]
INFO     Benchmark completed successfully
INFO     Results saved to: artifacts/Qwen_Qwen3-0.6B-chat-fixed-schedule/
            NVIDIA AIPerf | LLM Metrics
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┓
┃                     Metric ┃    avg ┃    min ┃    max ┃    p99 ┃    p50 ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━┩
│       Request Latency (ms) │ 389.45 │ 312.67 │ 466.23 │ 466.23 │ 389.45 │
│   Time to First Token (ms) │  89.12 │  71.34 │ 106.90 │ 106.90 │  89.12 │
│   Inter Token Latency (ms) │  16.78 │  14.23 │  19.34 │  19.34 │  16.78 │
│ Request Throughput (req/s) │   1.45 │      - │      - │      - │      - │
└────────────────────────────┴────────┴────────┴────────┴────────┴────────┘
JSON Export: artifacts/Qwen_Qwen3-0.6B-chat-fixed-schedule/profile_export_aiperf.json

Windowing Parameters:

  • --fixed-schedule-start-offset 2000: Start execution at 2000ms timestamp
  • --fixed-schedule-end-offset 4000: End execution at 4000ms timestamp

Use Cases

When to Use Fixed Schedule Benchmarking:

  • Traffic Replay: Reproduce exact timing patterns from production logs
  • Temporal Analysis: Study how performance varies with request timing
  • Peak Load Testing: Test system behavior during known high-traffic periods
  • SLA Validation: Verify performance under specific timing constraints
  • Capacity Planning: Model future load based on projected growth patterns
  • Regression Testing: Ensure temporal performance characteristics remain stable