ACE Documentation Index

ACE v0.1.0 - Adaptive Concurrency Engine

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Getting Started (30 minutes)

1. Install - pip install ace-concurrency
2. Quick Start - Run async example
3. Configuration - Customize AIMD parameters
4. Observability - Monitor via JSON logs

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Core Concepts

System Signals

Monitored every 2 seconds:

• CPU utilization (%)
• Memory utilization (%)
• Event loop lag (milliseconds)
• Queue depth (tasks waiting)
• Kafka consumer lag (optional)

Decision Types

• INCREASE: Proactive (low load) or reactive (queue catching up)
• DECREASE: High CPU/memory or queue overflow
• HOLD: Steady state, system healthy

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Integration Guides

Async Tasks

Best for: I/O-bound work (API calls, database queries)

async with ACEAsyncioManager(config) as mgr:
    await mgr.submit(your_async_task())

Thread Pools

Best for: CPU-bound work (calculations, data processing)

with ACEThreadPoolManager(config) as mgr:
    mgr.submit(your_sync_function)

Kafka Consumers

Best for: Event streaming with backpressure

async with ACEKafkaConsumerManager(config, "topic") as mgr:
    await mgr.process_async(handler)

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Configuration Guide

Basic Configuration

config = AIMDConfig(
    min_limit=1,
    max_limit=100,
    initial_limit=10,
)

Advanced Configuration

config = AIMDConfig(
    # Concurrency bounds
    min_limit=5,
    max_limit=200,
    initial_limit=50,

    # AIMD parameters
    increase_step=5,           # More aggressive increase
    decrease_factor=0.3,       # Faster decrease

    # Thresholds (CPU/Memory %)
    cpu_threshold=75.0,
    memory_threshold=80.0,
    cpu_increase_threshold=30.0,      # Earlier proactive scaling
    memory_increase_threshold=40.0,

    # Timing (seconds)
    adjustment_interval=1,     # More frequent decisions
    cooldown_period=10,        # Longer wait after decrease
)

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Monitoring & Observability

Structured JSON Logs

{
  "timestamp": "2026-01-16T14:30:45Z",
  "level": "INFO",
  "message": "AIMD decision",
  "decision": "INCREASE",
  "current_limit": 10,
  "new_limit": 12,
  "signals": {
    "cpu_percent": 35.5,
    "memory_percent": 42.3,
    "queue_depth": 5,
    "event_loop_lag_ms": 2.1
  }
}

Metrics to Track

• Current concurrency limit
• Active task count
• CPU/Memory utilization
• Decision frequency (INCREASE/DECREASE/HOLD)
• System response time

Version Information

Property	Value
Package	ace-concurrency
Version	0.1.0
Python	3.8+
License	MIT
Status	Production-Ready
PyPI	https://pypi.org/project/ace-concurrency/

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Common Tasks

Task: Change concurrency limits

# During runtime (if you need dynamic adjustment)
with ACEAsyncioManager(config) as mgr:
    # Limits controlled automatically by AIMD
    # To override, set directly:
    # Not recommended - trust AIMD algorithm
    pass

Task: Access current limit

async with ACEAsyncioManager(config) as mgr:
    print(f"Current limit: {mgr.current_limit}")
    print(f"Active tasks: {mgr.active_tasks}")

Task: Custom signal collection

from ace.core.signals import SignalCollector

collector = SignalCollector()
collector.set_queue_depth(len(my_queue))
signals = await collector.collect_all_async()

Task: Batch multiple AIMD managers

# Multiple independent managers (recommended for isolation)
async with ACEAsyncioManager(config1) as mgr1:
    async with ACEAsyncioManager(config2) as mgr2:
        # Separate control loops
        pass

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Support Resources

• Bug Reports: GitHub Issues
• Feature Requests: GitHub Discussions
• Code Examples: See intergrations/ directory