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# 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
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