config/cloud_agents.conf.example

# Cloud Agents Configuration
# Docking@HOME - AI Orchestration Settings

# === Agent Settings ===
agent_name = "DockingOrchestrator"
agent_version = "1.0.0"
enable_ai_orchestration = true     # Enable AI-powered task orchestration

# === Model Configuration ===
model_provider = "huggingface"     # huggingface, local
model_name = "OpenPeerAI/Cloud-Agents"
model_version = "latest"
model_temperature = 0.7            # Creativity (0.0 = deterministic, 1.0 = creative)
model_max_tokens = 2048            # Maximum tokens per response

# === API Settings ===
# Uncomment and set your API key if using cloud providers
# api_key = "YOUR_API_KEY_HERE"
# api_endpoint = "https://api.localhost:8080/v1"  # Custom endpoint if needed
api_timeout_seconds = 60
api_retries = 3

# === Task Orchestration ===
optimization_strategy = "adaptive"  # adaptive, greedy, balanced, ml
enable_load_balancing = true       # Enable intelligent load balancing
enable_auto_scaling = true         # Auto-scale worker allocation
predict_task_duration = true       # Use AI to predict task completion time
learn_from_history = true          # Learn from past executions

# === Resource Optimization ===
optimize_cpu_allocation = true     # Optimize CPU resource allocation
optimize_gpu_allocation = true     # Optimize GPU resource allocation
optimize_memory_usage = true       # Optimize memory allocation
optimize_network_bandwidth = false # Optimize network usage

# === Decision Making ===
decision_mode = "autonomous"       # autonomous, assisted, manual
confidence_threshold = 0.75        # Minimum confidence for autonomous decisions
require_human_approval = false     # Require approval for critical decisions
approval_timeout_seconds = 300     # Timeout for human approval

# === Task Prioritization ===
enable_smart_scheduling = true     # AI-based task scheduling
priority_factors = [
    "deadline",
    "resource_availability", 
    "task_complexity",
    "user_priority",
    "cost"
]
rebalance_interval_seconds = 60    # Seconds between priority rebalancing

# === Learning & Adaptation ===
enable_reinforcement_learning = true   # Enable RL for optimization
learning_rate = 0.001              # Learning rate for models
exploration_rate = 0.1             # Exploration vs exploitation (epsilon)
memory_size = 10000                # Experience replay memory size
batch_size = 32                    # Training batch size
update_frequency = 100             # Model update frequency (steps)

# === Performance Metrics ===
track_metrics = true               # Track performance metrics
metrics_to_track = [
    "task_completion_time",
    "resource_utilization",
    "success_rate",
    "cost_per_task",
    "throughput"
]

# === Prediction Models ===
enable_task_prediction = true      # Predict task requirements
enable_failure_prediction = true   # Predict potential failures
enable_bottleneck_detection = true # Detect performance bottlenecks
prediction_confidence_threshold = 0.70

# === Cost Optimization ===
enable_cost_optimization = true    # Optimize operational costs
cost_per_cpu_hour = 0.05          # Cost per CPU hour (USD)
cost_per_gpu_hour = 0.50          # Cost per GPU hour (USD)
cost_per_gb_storage = 0.01        # Cost per GB storage per month (USD)
budget_limit_daily = 100.0        # Daily budget limit (USD)

# === Auto-scaling ===
min_workers = 1                    # Minimum worker nodes
max_workers = 100                  # Maximum worker nodes
scale_up_threshold = 0.80         # Resource usage to trigger scale up
scale_down_threshold = 0.30       # Resource usage to trigger scale down
scale_up_increment = 2            # Workers to add when scaling up
scale_down_increment = 1          # Workers to remove when scaling down
cooldown_period_seconds = 300     # Cooldown between scaling operations

# === Anomaly Detection ===
enable_anomaly_detection = true   # Detect anomalies in execution
anomaly_threshold = 3.0           # Standard deviations for anomaly
alert_on_anomaly = true           # Send alerts on detected anomalies

# === Collaboration ===
enable_multi_agent = false        # Enable multi-agent coordination
agent_communication_protocol = "rest"  # rest, grpc, mqtt
coordinator_url = "http://localhost:9000"

# === Caching & State ===
cache_predictions = true          # Cache AI predictions
cache_duration_seconds = 3600    # Cache duration
state_persistence = true         # Persist agent state
state_file = "agent_state.json"  # State file path
checkpoint_interval_minutes = 10 # Save checkpoint interval

# === Monitoring & Observability ===
enable_telemetry = true          # Enable telemetry
telemetry_endpoint = "http://localhost:4318"  # OpenTelemetry endpoint
log_level = "INFO"               # DEBUG, INFO, WARNING, ERROR
log_predictions = true           # Log AI predictions
log_decisions = true            # Log orchestration decisions

# === Rate Limiting ===
max_requests_per_minute = 100   # Max API requests per minute
max_concurrent_predictions = 10 # Max concurrent predictions

# === Fallback Behavior ===
fallback_to_manual = true       # Fallback to manual on AI failure
fallback_strategy = "conservative"  # conservative, aggressive, balanced
retry_failed_predictions = true
max_prediction_retries = 3

# === Feature Flags ===
features = {
    "smart_routing": true,
    "predictive_scaling": true,
    "cost_optimization": true,
    "anomaly_detection": true,
    "adaptive_learning": true,
    "multi_objective_optimization": true
}

# === Experimental Features ===
experimental_features = {
    "quantum_optimization": false,
    "federated_learning": false,
    "neural_architecture_search": false
}

# === Security ===
encrypt_model_data = false      # Encrypt model data at rest
secure_inference = false        # Use secure inference (TEE)
audit_decisions = true         # Audit all AI decisions