Distributed Tasks

24. Distributed Task System Design¶

24.1 System Overview¶

The Distributed Task System serves as the large-scale computation distribution engine for the quantitative trading system, enabling massive parallel processing of computationally intensive tasks such as backtesting, strategy optimization, and simulation. This system provides horizontal scalability and fault tolerance through distributed task execution across multiple nodes.

24.1.1 Core Objectives¶

Massive Parallel Processing: - Large-Scale Execution: Support for thousands of concurrent tasks (backtesting, optimization) - Automatic Distribution: Intelligent task distribution across multiple nodes - Resource Optimization: Efficient utilization of distributed computing resources - Scalable Architecture: Linear scaling with additional worker nodes

Fault Tolerance and Reliability: - Automatic Retry: Failed task recovery with configurable retry policies - Node Failure Handling: Automatic task migration when nodes fail - Task Dependencies: Support for complex task dependency chains - Priority Management: Task priority and resource allocation control

24.2 Architecture Design¶

24.2.1 Microservice Architecture¶

Distributed Task Center Service:

services/distributed-task-center/
├── src/
│   ├── main.py                 # Service entry point
│   ├── scheduler/              # Central task scheduler
│   │   ├── task_manager.py     # Task lifecycle management
│   ├── dispatcher/             # Task distribution module
│   │   ├── nats_dispatcher.py  # NATS-based task distribution
│   ├── worker/                 # Worker execution framework
│   │   ├── task_worker.py      # Task execution engine
│   ├── monitor/                # Task monitoring and tracking
│   │   ├── task_monitor.py     # Task status tracking
│   ├── api/                    # REST API interface
│   │   ├── task_api.py         # Task management endpoints
│   ├── config.py               # Configuration management
│   ├── requirements.txt        # Dependencies
├── Dockerfile                  # Container configuration

24.2.2 Core Components¶

Central Task Scheduler: - Task Lifecycle Management: Task creation, scheduling, and completion tracking - Resource Allocation: Intelligent distribution of tasks across available workers - Dependency Management: Task dependency resolution and execution ordering - Priority Handling: Task priority-based scheduling and resource allocation

Task Distribution Bus: - NATS Integration: High-performance message bus for task distribution - Load Balancing: Intelligent task distribution across worker nodes - Fault Tolerance: Automatic failover and task redistribution - Scalability: Linear scaling with additional distribution nodes

Worker Node Pool: - Task Execution: Isolated task execution environments - Resource Management: CPU, memory, and storage allocation per task - Health Monitoring: Worker node health and performance tracking - Auto-Scaling: Dynamic worker node scaling based on workload

Status Tracking System: - Real-time Monitoring: Live task status and progress tracking - Performance Metrics: Task execution time and resource usage - Failure Analysis: Detailed error logging and analysis - Historical Records: Complete task execution history

24.3 Task Categories and Execution¶

24.3.1 Computational Tasks¶

Backtesting Tasks: - Strategy Backtesting: Historical strategy performance evaluation - Parameter Sweeping: Large-scale parameter optimization - Market Simulation: Multi-market scenario testing - Performance Analysis: Comprehensive performance metrics calculation

Optimization Tasks: - Strategy Optimization: Genetic algorithm and machine learning optimization - Portfolio Optimization: Multi-objective portfolio optimization - Risk Optimization: Risk parameter calibration and optimization - Execution Optimization: Order execution strategy optimization

Simulation Tasks: - Monte Carlo Simulation: Probabilistic market scenario simulation - Stress Testing: Extreme market condition testing - Scenario Analysis: What-if analysis for different market conditions - Model Validation: Statistical model validation and testing

24.3.2 Task Characteristics¶

Task Dependencies: - Sequential Dependencies: Tasks that must execute in order - Parallel Dependencies: Tasks that can execute simultaneously - Conditional Dependencies: Tasks that depend on specific conditions - Resource Dependencies: Tasks that require specific resources

Task Priorities: - High Priority: Critical tasks requiring immediate execution - Normal Priority: Standard tasks with normal scheduling - Low Priority: Background tasks with lower resource allocation - Batch Priority: Batch processing tasks for non-critical operations

24.4 Technology Stack¶

24.4.1 Core Technologies¶

Message Bus: - NATS: High-performance messaging for task distribution - NATS Streaming: Persistent message streaming for reliability - NATS JetStream: Advanced streaming with consumer groups - Message Serialization: JSON and Protocol Buffers for task data

Task Execution: - Docker Containers: Isolated task execution environments - Kubernetes: Container orchestration and scaling - Resource Limits: CPU and memory allocation per task - Network Isolation: Secure task execution network

Monitoring and Logging: - Prometheus: Task execution metrics collection - Grafana: Task monitoring dashboards - Structured Logging: Comprehensive task execution logs - Distributed Tracing: Task execution flow tracking

24.4.2 Integration Technologies¶

Data Storage: - PostgreSQL: Task metadata and execution history - Redis: Task status caching and session management - Time-Series DB: Task performance metrics storage - Object Storage: Large task output file storage

External Integrations: - Backtest Engine: Integration with backtesting services - Strategy Optimizer: Integration with optimization services - Data Services: Integration with market data services - Notification Services: Integration with alerting systems

24.5 API Design¶

24.5.1 Task Management Endpoints¶

Task Submission:

POST   /api/v1/tasks/submit              # Submit new task
POST   /api/v1/tasks/batch-submit        # Submit multiple tasks
GET    /api/v1/tasks/list                # List all tasks
GET    /api/v1/tasks/{task_id}           # Get specific task

Task Control:

POST   /api/v1/tasks/{task_id}/cancel    # Cancel running task
POST   /api/v1/tasks/{task_id}/retry     # Retry failed task
POST   /api/v1/tasks/{task_id}/pause     # Pause task execution
POST   /api/v1/tasks/{task_id}/resume    # Resume paused task

Task Monitoring:

GET    /api/v1/tasks/{task_id}/status    # Get task status
GET    /api/v1/tasks/{task_id}/logs      # Get task execution logs
GET    /api/v1/tasks/{task_id}/metrics   # Get task performance metrics
GET    /api/v1/tasks/statistics          # Get overall task statistics

24.5.2 Worker Management Endpoints¶

Worker Control:

GET    /api/v1/workers/list              # List all worker nodes
GET    /api/v1/workers/{worker_id}       # Get worker status
POST   /api/v1/workers/{worker_id}/stop  # Stop worker node
POST   /api/v1/workers/scale             # Scale worker pool

Real-time Updates:

/ws/tasks/status                         # Real-time task status updates
/ws/tasks/execution                      # Real-time execution events
/ws/workers/status                       # Real-time worker status
/ws/tasks/alerts                         # Task failure and performance alerts

24.6 Frontend Integration¶

24.6.1 Task Management Dashboard¶

Task Overview Panel: - Task List: Comprehensive view of all submitted tasks - Status Indicators: Visual task status and progress indicators - Priority Display: Task priority and resource allocation - Quick Actions: Task control and management actions

Task Execution Panel: - Real-time Monitoring: Live task execution status - Execution History: Historical task execution records - Performance Metrics: Task execution time and success rates - Failure Analysis: Detailed error logs and troubleshooting

Worker Management Panel: - Worker Status: Real-time worker node health and performance - Resource Usage: CPU, memory, and storage utilization - Load Distribution: Task distribution across worker nodes - Scaling Controls: Dynamic worker pool scaling

24.6.2 Interactive Features¶

Visualization Tools: - Task Timeline: Visual representation of task schedules - Execution Flow: Task dependency and execution flow diagrams - Performance Charts: Task execution performance trends - Resource Usage: Worker resource consumption visualization

Management Tools: - Bulk Operations: Multi-task submission and management - Template Management: Reusable task template library - Scheduling Optimization: Intelligent task scheduling suggestions - Resource Planning: Resource allocation and capacity planning

24.7 Scalability and Performance¶

24.7.1 Horizontal Scaling¶

Worker Node Scaling: - Auto-Scaling: Dynamic scaling based on workload - Load Balancing: Intelligent task distribution - Resource Optimization: Efficient resource utilization - Geographic Distribution: Multi-region worker deployment

Performance Characteristics: - Task Throughput: 10,000+ concurrent tasks - Execution Speed: Sub-second task distribution - Resource Efficiency: 90%+ resource utilization - Fault Tolerance: 99.9%+ task completion rate

24.7.2 Fault Tolerance¶

Failure Recovery: - Automatic Retry: Configurable retry policies - Task Migration: Automatic task redistribution on node failure - Data Persistence: Task state persistence across failures - Health Monitoring: Continuous health checking and recovery

Reliability Features: - Message Persistence: NATS streaming for message reliability - State Management: Distributed state management - Backup Systems: Redundant task execution systems - Disaster Recovery: Complete system recovery capabilities

24.8 Implementation Roadmap¶

24.8.1 Phase 1: Foundation (Weeks 1-2)¶

• Basic Task Manager: Core task lifecycle management
• NATS Integration: Basic task distribution via NATS
• Simple Workers: Basic worker node implementation
• Simple API: Basic task management endpoints

24.8.2 Phase 2: Advanced Features (Weeks 3-4)¶

• Task Dependencies: Dependency management and resolution
• Priority System: Task priority and resource allocation
• Retry Mechanism: Automatic failure recovery
• Monitoring Integration: Comprehensive task monitoring

24.8.3 Phase 3: Scalability (Weeks 5-6)¶

• Auto-Scaling: Dynamic worker pool scaling
• Load Balancing: Intelligent task distribution
• Performance Optimization: Task execution optimization
• Advanced Monitoring: Real-time performance analytics

24.8.4 Phase 4: Production Ready (Weeks 7-8)¶

• Enterprise Features: Advanced enterprise capabilities
• Multi-Region Support: Geographic distribution
• Advanced Analytics: Comprehensive performance analytics
• Integration Ecosystem: Full system integration

24.9 Integration with Existing System¶

24.9.1 Service Integration¶

Backtest Engine Integration:

Distributed Task System → Backtest Tasks → Backtest Engine → Performance Results

Strategy Optimizer Integration:

Distributed Task System → Optimization Tasks → Strategy Optimizer → Optimized Parameters

Portfolio Optimization Integration:

Distributed Task System → Portfolio Tasks → Portfolio Optimizer → Optimal Weights

24.9.2 Data Flow Integration¶

Task Execution Events: - Task Submission: Task creation and scheduling events - Task Execution: Task execution start and progress events - Task Completion: Successful task completion events - Task Failure: Task failure and error events

Resource Management: - Resource Allocation: Worker resource allocation and management - Load Distribution: Task load distribution across workers - Performance Monitoring: Task and worker performance tracking - Capacity Planning: Resource capacity planning and optimization

24.10 Business Value¶

24.10.1 Computational Power¶

24.10.2 Operational Excellence¶