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Systems & SRE¶

ArqonHPO keeps infrastructure systems stable by continuously tuning operational parameters as conditions change.

The Challenge¶

Static configuration drifts from optimal as:

Traffic patterns shift (day/night, seasonal)
Hardware ages (slower disks, thermal throttling)
Dependencies change (upstream latency, API limits)

Manual tuning can't keep up. ArqonHPO automates it.

What to Optimize¶

Database Connections¶

Parameter	Impact
`pool_size`	Throughput vs. connection overhead
`max_idle_time`	Resource usage vs. cold start
`connection_timeout`	Resilience vs. latency

Caching¶

Parameter	Impact
`ttl_seconds`	Freshness vs. hit rate
`max_memory_mb`	Hit rate vs. eviction
`eviction_policy`	Workload-specific optimization

Queue Management¶

Parameter	Impact
`batch_size`	Throughput vs. latency
`prefetch_count`	Throughput vs. memory
`visibility_timeout`	Reliability vs. reprocessing

Example: Connection Pool Tuning¶

import json
from arqonhpo import ArqonSolver

config = {
    "seed": 42,
    "budget": 500,
    "bounds": {
        "pool_size": {"min": 5, "max": 100},
        "max_idle_time_s": {"min": 30, "max": 600},
        "connection_timeout_ms": {"min": 100, "max": 5000}
    }
}

solver = ArqonSolver(json.dumps(config))

while True:
    candidate = solver.ask_one()
    if candidate is None:
        break

    # Apply new pool config (hot reload)
    db_pool.reconfigure(
        pool_size=int(candidate["pool_size"]),
        max_idle_time=candidate["max_idle_time_s"],
        timeout=candidate["connection_timeout_ms"]
    )

    # Observe for 5 minutes
    metrics = observe_db_metrics(duration_s=300)

    # Multi-objective: high throughput, low p99, low errors
    score = (
        metrics.queries_per_second * 10
        - metrics.p99_latency_ms
        - metrics.error_rate * 1000
    )

    solver.seed(json.dumps([{
        "params": candidate,
        "value": -score,  # Minimize negative score = maximize
        "cost": 300.0
    }]))

Example: JVM GC Tuning¶

config = {
    "seed": 42,
    "budget": 100,
    "bounds": {
        "heap_size_gb": {"min": 4, "max": 32},
        "new_ratio": {"min": 1, "max": 8},
        "survivor_ratio": {"min": 2, "max": 32},
        "max_gc_pause_ms": {"min": 50, "max": 500}
    }
}

# Apply via JVM flags:
# -Xmx{heap_size}g -XX:NewRatio={new_ratio} ...

Observability Integration¶

ArqonHPO exports Prometheus metrics:

# Track optimization progress
arqon_history_len
arqon_ask_total
arqon_tell_total

# Monitor for regressions
rate(arqon_eval_duration_seconds_sum[5m]) / rate(arqon_eval_duration_seconds_count[5m])

Safety for Production¶

Guardrails prevent runaway configurations:

{
  "max_delta": [10, 60, 500],
  "max_updates_per_second": 0.1,
  "rollback_on_regression": true
}

10 connections max change per update
1 update per 10 seconds max
Auto-rollback if p99 spikes

Next Steps¶