Distributed Task Scheduling System

Software Team Project

Overview

Team-developed microservices-based task scheduling system with fault tolerance and horizontal scalability.

Technologies Used

  • Go
  • gRPC
  • Kubernetes
  • etcd
  • Prometheus
  • Grafana

Links

Project Overview

Our team built a highly available, distributed task scheduling system capable of managing millions of tasks across a cluster of nodes. The system is designed for high throughput, fault tolerance, and horizontal scalability.

My Contributions

As the Backend Architecture lead, I focused on:

  • Designing the core scheduling algorithms
  • Implementing the task queue and distribution logic
  • Building the consensus mechanism for distributed coordination
  • Performance optimization and load testing

System Architecture

Core Components

  1. Scheduler Service: Assigns tasks to worker nodes based on load and capacity
  2. Worker Pool: Executes tasks with configurable concurrency
  3. Coordinator: Uses etcd for distributed consensus and leader election
  4. API Gateway: Provides REST and gRPC interfaces for task submission
  5. Monitoring: Prometheus metrics and Grafana dashboards

Design Principles

  • Fault Tolerance: System continues operating even with node failures
  • Horizontal Scalability: Add more nodes to increase capacity
  • At-Least-Once Delivery: Tasks are guaranteed to execute at least once
  • Priority Scheduling: Support for task priorities and deadlines

Technical Implementation

Scheduling Algorithm

We implemented a sophisticated scheduling algorithm that considers:

  • Worker node capacity and current load
  • Task priorities and deadlines
  • Data locality for improved performance
  • Fair resource allocation across tenants

Distributed Coordination

Used etcd for:

  • Leader election among scheduler nodes
  • Distributed locking for task assignment
  • Configuration management
  • Service discovery

Communication

  • gRPC for high-performance inter-service communication
  • Protocol Buffers for efficient serialization
  • Connection pooling for reduced latency
  • Retry logic with exponential backoff

Fault Tolerance Features

Task Reliability

  • Automatic retry with configurable policies
  • Task timeout detection and recovery
  • Dead letter queue for failed tasks
  • Checkpointing for long-running tasks

System Resilience

  • Leader election ensures continuous operation
  • Health checks and automatic node removal
  • Graceful shutdown and task migration
  • Backup scheduler nodes for high availability

Performance Optimization

Achieved Performance Metrics

  • Throughput: 100,000 tasks/second
  • Latency: p50: 5ms, p99: 50ms
  • Scalability: Linear scaling up to 100 worker nodes
  • Availability: 99.95% uptime

Optimization Techniques

  • Lock-free data structures where possible
  • Batch processing for efficiency
  • Connection pooling and multiplexing
  • Memory-efficient task representation

Monitoring and Observability

Comprehensive monitoring includes:

  • Task submission and completion rates
  • Worker node health and resource usage
  • Queue depth and processing latency
  • Error rates and types
  • Custom business metrics

Grafana dashboards provide real-time visibility into:

  • System health overview
  • Performance metrics
  • Resource utilization
  • Alert status

Testing Strategy

Unit Testing

  • Comprehensive unit tests for core logic
  • Mock frameworks for external dependencies
  • Test coverage >85%

Integration Testing

  • End-to-end workflow tests
  • Multi-node cluster tests
  • Failure scenario testing

Load Testing

  • Simulated realistic workloads
  • Stress testing to find limits
  • Performance regression testing

Deployment

Deployed on Kubernetes with:

  • Helm charts for easy deployment
  • ConfigMaps for configuration management
  • Horizontal Pod Autoscaling
  • Rolling updates with zero downtime

Real-world Usage

The system has been successfully used for:

  • Batch data processing pipelines
  • Scheduled report generation
  • Webhook delivery systems
  • Video transcoding workflows

Team Collaboration Highlights

This project exemplified effective team collaboration:

  • Agile methodology with 2-week sprints
  • Daily standups and weekly retrospectives
  • Shared on-call rotation
  • Comprehensive documentation in Confluence
  • Regular architecture review sessions

Challenges Overcome

Challenge: Race conditions in distributed task assignment Solution: Implemented distributed locking with etcd and idempotent operations

Challenge: Memory consumption with millions of queued tasks Solution: Implemented tiered storage with hot and cold queues

Challenge: Monitoring overhead at scale Solution: Sampling and aggregation strategies for metrics collection

Future Roadmap

  • Support for DAG (Directed Acyclic Graph) workflows
  • Advanced scheduling algorithms (gang scheduling, bin packing)
  • Multi-region deployment for geo-distributed systems
  • Integration with popular workflow engines (Airflow, Temporal)

Open Source

While the core system is proprietary, we’ve open-sourced several components:

  • Task priority queue implementation
  • gRPC connection pool library
  • Prometheus metrics helper library

Lessons Learned

  • Start with observability from day one
  • Design for failure from the beginning
  • Document architectural decisions (ADRs)
  • Regular load testing prevents surprises
  • Team communication is as important as technical skills

Team Members

  • Alejandro Arteaga (Backend Architecture)
  • David Kim (Infrastructure)
  • Lisa Wang (Monitoring & Observability)
  • James Thompson (API Design)