[Epic] Long-Running Task Execution - Support for Hours/Days Duration Tasks

[adiasaf]

What You Want

Enable CUGA to execute long-running tasks that may take hours or days to complete, with robust state management, checkpointing, resumability, and monitoring capabilities. This allows CUGA to handle complex, time-intensive workflows without timeout issues or resource constraints.

Key Objectives

1. Asynchronous Execution

   • Execute tasks asynchronously without blocking
   • Support background task processing
   • Enable task queuing and scheduling

2. State Persistence & Checkpointing

   • Save agent state at regular intervals
   • Resume from last checkpoint on failure
   • Maintain execution history and audit trail

3. Resilience & Recovery

   • Automatic retry on transient failures
   • Graceful handling of interruptions
   • Recovery from system crashes or restarts

4. Monitoring & Control

   • Real-time progress tracking
   • Task cancellation and pause/resume
   • Resource usage monitoring
   • Alerting on issues or completion

Why You Need It

Business Value

• Complex Workflows: Handle multi-step processes that take hours or days
• Reliability: Complete tasks despite interruptions or failures
• Resource Efficiency: Free up resources while tasks run in background
• Scalability: Process multiple long-running tasks concurrently
• User Experience: Non-blocking operations, users don't wait for completion

Technical Value

• Fault Tolerance: Survive system failures and restarts
• State Management: Maintain consistent state across long durations
• Resource Optimization: Efficient use of compute and memory
• Observability: Track progress and diagnose issues
• Flexibility: Pause, resume, or cancel tasks as needed

Use Cases

• Data Processing: Large-scale ETL, data migration, batch processing
• Research & Analysis: Long-running simulations, data analysis
• Content Generation: Bulk document generation, video processing
• System Monitoring: Continuous monitoring and alerting
• Scheduled Tasks: Daily/weekly reports, periodic maintenance

How It Could Work

Phase 1: Asynchronous Task Framework

1. Task Queue System

   from cuga.tasks import LongRunningTask, TaskQueue
   
   # Create long-running task
   task = LongRunningTask(
       name="process_large_dataset",
       agent_config="agent_config.yaml",
       input_data={"dataset_path": "/data/large.csv"},
       estimated_duration="6 hours"
   )
   
   # Submit to queue
   queue = TaskQueue()
   task_id = queue.submit(task)
   
   # Task runs asynchronously
   print(f"Task submitted: {task_id}")

2. Background Worker Pool

   • Dedicated worker processes for long-running tasks
   • Configurable worker count and resource limits
   • Load balancing across workers
   • Worker health monitoring

3. Task Scheduling

   • Schedule tasks for future execution
   • Cron-like scheduling for recurring tasks
   • Priority-based task execution
   • Dependency management between tasks

4. Task Lifecycle Management

   • States: PENDING, RUNNING, PAUSED, COMPLETED, FAILED, CANCELLED
   • State transitions and validation
   • Lifecycle hooks for custom logic

Phase 2: State Persistence & Checkpointing

1. Automatic Checkpointing

   # Agent automatically saves checkpoints
   agent = CugaAgent(
       checkpoint_interval="5 minutes",
       checkpoint_storage="s3://bucket/checkpoints/"
   )
   
   # Checkpoint includes:
   # - Agent state
   # - Execution progress
   # - Tool outputs
   # - Memory/context
   # - Timestamp

2. Resume from Checkpoint

   # Resume failed or interrupted task
   task = LongRunningTask.resume(task_id)
   # → Loads last checkpoint
   # → Continues from where it left off

3. Checkpoint Storage

   • Support multiple storage backends (S3, GCS, local filesystem, database)
   • Compression and encryption
   • Retention policies and cleanup
   • Versioning and rollback

4. Incremental Progress Tracking

   • Track completion percentage
   • Estimate remaining time
   • Record milestones and sub-tasks
   • Progress visualization

Phase 3: Resilience & Recovery

1. Automatic Retry Logic

   • Configurable retry policies
   • Exponential backoff
   • Maximum retry attempts
   • Retry on specific error types

2. Failure Handling

   task = LongRunningTask(
       name="resilient_task",
       retry_policy={
           "max_attempts": 3,
           "backoff": "exponential",
           "retry_on": ["NetworkError", "TimeoutError"]
       },
       on_failure="notify_admin"
   )

3. Graceful Shutdown

   • Save checkpoint before shutdown
   • Complete current operation
   • Clean up resources
   • Mark task as PAUSED for later resume

4. Dead Letter Queue

   • Failed tasks moved to DLQ
   • Manual review and retry
   • Error analysis and debugging

Phase 4: Monitoring & Control

1. Real-Time Progress Tracking

   # Get task status
   status = task.get_status()
   print(f"Progress: {status.progress}%")
   print(f"Elapsed: {status.elapsed_time}")
   print(f"ETA: {status.estimated_completion}")
   print(f"Current step: {status.current_step}")

2. Task Control API

   # Pause task
   task.pause()
   
   # Resume task
   task.resume()
   
   # Cancel task
   task.cancel(reason="User requested")
   
   # Get logs
   logs = task.get_logs(last_n=100)

3. Monitoring Dashboard

   • Web UI for task monitoring
   • Real-time progress visualization
   • Task history and analytics
   • Resource usage graphs
   • Alert configuration

4. Notifications & Alerts

   • Email/Slack notifications on completion or failure
   • Webhook callbacks for task events
   • Custom alerting rules
   • Integration with monitoring systems (PagerDuty, etc.)

Phase 5: Advanced Features

1. Distributed Execution

   • Split task across multiple workers
   • Parallel sub-task execution
   • Result aggregation
   • Distributed state management

2. Resource Management

   • CPU and memory limits per task
   • GPU allocation for ML tasks
   • Disk space monitoring
   • Network bandwidth throttling

3. Task Dependencies

   • Define task dependencies (DAG)
   • Conditional execution
   • Fan-out/fan-in patterns
   • Workflow orchestration

4. Time-Based Features

   • Task timeout configuration
   • Scheduled execution windows
   • Rate limiting
   • Time-based checkpointing

Sub-Tasks

This epic should be broken down into individual issues for:

Core Framework

• Design long-running task architecture
• Implement task queue system
• Build background worker pool
• Add task scheduling capabilities
• Create task lifecycle management

State Management

• Design checkpoint format and storage
• Implement automatic checkpointing
• Add resume from checkpoint functionality
• Build progress tracking system
• Create state persistence layer

Resilience

• Implement retry logic and policies
• Add failure handling mechanisms
• Build graceful shutdown system
• Create dead letter queue
• Add error recovery strategies

Monitoring & Control

• Build task status API
• Implement task control operations (pause/resume/cancel)
• Create monitoring dashboard
• Add logging and audit trail
• Build notification system

Advanced Features

• Add distributed execution support
• Implement resource management
• Build task dependency system
• Add time-based features
• Create workflow orchestration

Testing & Documentation

• Create long-running task examples
• Write integration tests
• Add performance benchmarks
• Write user documentation
• Create operational runbook

Success Metrics

Must Achieve:

• ✅ Execute tasks running for 24+ hours without failure
• ✅ Resume from checkpoint with <1% data loss
• ✅ Survive system restarts and crashes
• ✅ Support 100+ concurrent long-running tasks
• ✅ <5 minute recovery time from failures
• ✅ Real-time progress tracking with <10 second latency

Nice to Have:

• Support for tasks running weeks/months
• Sub-second checkpoint overhead
• Distributed execution across multiple machines
• Visual workflow designer
• Predictive failure detection

Technical Considerations

Architecture Decisions

1. Task Queue Backend

   • Options: Celery, RQ, custom implementation
   • Consider: Redis, RabbitMQ, AWS SQS, Kafka
   • Trade-offs: complexity vs features

2. Checkpoint Storage

   • Local filesystem for development
   • S3/GCS for production
   • Database for metadata
   • Hybrid approach

3. State Serialization

   • Pickle for Python objects
   • JSON for interoperability
   • Protobuf for efficiency
   • Custom serialization for complex types

4. Worker Architecture

   • Process-based workers (multiprocessing)
   • Container-based workers (Docker/Kubernetes)
   • Serverless workers (AWS Lambda, Azure Functions)

Challenges & Solutions

Challenge: State size grows over time

• Solution: Incremental checkpoints, state compression, garbage collection

Challenge: Checkpoint overhead impacts performance

• Solution: Async checkpointing, configurable intervals, smart checkpointing

Challenge: Coordinating distributed tasks

• Solution: Distributed locks, consensus algorithms, event-driven coordination

Challenge: Resource exhaustion

• Solution: Resource limits, monitoring, auto-scaling, task prioritization

Links and Context

Task Queue Systems

• Celery - Distributed task queue
• RQ - Simple Python task queue
• Temporal - Durable execution platform
• Airflow - Workflow orchestration

Related Components

• CUGA agent: src/cuga/backend/cuga_graph/
• State management: src/cuga/backend/cuga_graph/state/
• Memory: src/cuga/backend/cuga_graph/nodes/save_reuse/

Similar Projects

• Prefect - Workflow orchestration
• Dagster - Data orchestration
• Argo Workflows - Kubernetes-native workflows

Example Use Cases

Use Case 1: Large-Scale Data Processing

Task: Process 1TB dataset
Duration: 12 hours
Checkpoints: Every 30 minutes
Recovery: Resume from last checkpoint on failure

Use Case 2: Continuous Monitoring

Task: Monitor system metrics 24/7
Duration: Indefinite
Checkpoints: Hourly
Actions: Alert on anomalies, generate daily reports

Use Case 3: Scheduled Batch Job

Task: Generate monthly reports
Schedule: 1st of each month at 2 AM
Duration: 4 hours
Notifications: Email on completion

Implementation Timeline

• Phase 1 (Weeks 1-4): Core async framework and task queue
• Phase 2 (Weeks 5-8): State persistence and checkpointing
• Phase 3 (Weeks 9-12): Resilience and recovery mechanisms
• Phase 4 (Weeks 13-16): Monitoring, control, and dashboard
• Phase 5 (Weeks 17-20): Advanced features and optimization

Dependencies

• Task queue backend (Celery/RQ or custom)
• Persistent storage (S3, database)
• Monitoring infrastructure
• Container orchestration (optional, for distributed execution)
• Message broker (Redis, RabbitMQ, or Kafka)