Eden Task: Distributed Task Orchestration

Press Release

Ataiva Unveils Eden Task: Next-Generation Distributed Task Orchestration for High-Performance Computing

For immediate release - June 3, 2025

Sub-headline

Eden Task revolutionizes distributed computing with intelligent task orchestration, Redis-powered queuing, and seamless container integration, enabling teams to scale compute-intensive workloads efficiently while maintaining reliability and cost optimization.

The Problem

Modern applications require massive computational power for CI/CD pipelines, data processing, machine learning training, and batch operations. Traditional task execution systems struggle with the complexity of distributed computing - they either lack the sophistication to handle complex dependencies and resource requirements, or they’re so complex that they require dedicated teams to operate. Teams often resort to custom solutions that don’t scale, lack monitoring, or fail to optimize resource utilization, leading to wasted compute costs and unreliable processing. The challenge is compounded by the need to work across multiple environments - from local development to cloud-native Kubernetes clusters - while maintaining consistent behavior and performance.

The Solution

Eden Task addresses these challenges by providing a comprehensive distributed task orchestration platform that combines simplicity with enterprise-grade capabilities. Built on Redis for high-performance queuing and designed for seamless integration with container orchestrators, Eden Task intelligently manages task distribution, resource allocation, and execution across heterogeneous environments. The system automatically handles task dependencies, retry logic, and resource optimization while providing real-time monitoring and comprehensive analytics. Whether running CI/CD builds, processing data pipelines, or executing machine learning workloads, Eden Task ensures optimal resource utilization and reliable execution at any scale.

Community Quote

“Eden Task has transformed how we handle our compute-intensive workloads,” says David Chen, Principal Engineer at DataScale Analytics. “We were struggling with a custom task system that couldn’t handle our growing ML training workloads and data processing pipelines. Eden Task gave us enterprise-grade orchestration without the operational complexity. Our job completion rate improved from 78% to 99.5%, and we reduced our compute costs by 35% through intelligent resource allocation. The Redis-based queuing handles our peak loads of 10,000+ concurrent tasks without breaking a sweat. What used to require a dedicated team to maintain now runs itself, letting our engineers focus on building features instead of managing infrastructure.”

How It Works

Eden Task implements distributed task orchestration through a sophisticated architecture designed for performance, reliability, and scalability:

Redis-Powered Queuing: High-performance task queuing built on Redis Streams provides reliable message delivery, automatic persistence, and horizontal scaling. The system handles millions of tasks per hour while maintaining low latency and ensuring no task loss.

Intelligent Scheduling: Advanced scheduling algorithms consider resource requirements, dependencies, priorities, and historical performance data to optimize task placement and execution timing. The system automatically balances load across available workers and can scale resources dynamically based on queue depth.

Container-Native Execution: Seamless integration with Docker, Kubernetes, and other container orchestrators enables tasks to run in isolated, reproducible environments. The system automatically handles container lifecycle management, resource allocation, and cleanup.

Dependency Management: Sophisticated dependency resolution supports complex task graphs with conditional execution, fan-out/fan-in patterns, and dynamic dependency calculation. Tasks can depend on data, other tasks, external events, or time-based conditions.

Resource Optimization: Intelligent resource allocation considers CPU, memory, storage, and network requirements to optimize placement and minimize costs. The system can automatically scale worker pools and negotiate with cloud providers for optimal pricing.

Comprehensive Monitoring: Real-time visibility into task execution with detailed metrics, performance analytics, and predictive insights. The system tracks resource utilization, execution patterns, and failure modes to continuously optimize performance.

Availability

Eden Task is available as part of the Eden DevOps Suite, currently in Phase 2 completion with production-ready capabilities. The component can be used standalone or as part of the integrated Eden platform. Visit the GitHub repository for documentation and access information.

Get Started Today

Scale your computational workloads with Eden Task’s intelligent distributed orchestration. Experience the power of enterprise-grade task management that adapts to your needs and optimizes your resources automatically.

Frequently Asked Questions

Task Orchestration Questions

What types of tasks can Eden Task handle?
Eden Task is designed for a wide range of computational workloads:

• CI/CD Operations: Build, test, and deployment tasks with complex dependencies
• Data Processing: ETL pipelines, data transformation, and batch processing jobs
• Machine Learning: Model training, hyperparameter tuning, and inference pipelines
• Scientific Computing: Simulation, analysis, and computational research workloads
• Media Processing: Video encoding, image processing, and content generation
• Scheduled Operations: Maintenance tasks, backups, and periodic data operations

How does Eden Task handle task dependencies?
Eden Task provides sophisticated dependency management:

# Task definition with dependencies
name: "ML Training Pipeline"
tasks:
  - name: "data-preprocessing"
    image: "python:3.9"
    command: ["python", "preprocess.py"]
    resources:
      cpu: "2"
      memory: "4Gi"
      
  - name: "feature-engineering"
    image: "python:3.9"
    command: ["python", "features.py"]
    depends_on: ["data-preprocessing"]
    resources:
      cpu: "4"
      memory: "8Gi"
      
  - name: "model-training"
    image: "tensorflow/tensorflow:latest-gpu"
    command: ["python", "train.py"]
    depends_on: ["feature-engineering"]
    resources:
      cpu: "8"
      memory: "32Gi"
      gpu: "1"

Can tasks run in parallel?
Yes, Eden Task excels at parallel execution:

• Automatic Parallelization: Independent tasks run concurrently
• Fan-out Patterns: Single task can spawn multiple parallel subtasks
• Resource-Aware Scheduling: Parallel execution respects resource constraints
• Dynamic Scaling: Worker pools scale based on parallel task demand

How does Eden Task ensure task reliability?
Eden Task implements multiple reliability mechanisms:

• Automatic Retries: Configurable retry policies with exponential backoff
• Health Monitoring: Continuous health checks for running tasks
• Failure Recovery: Automatic recovery from worker failures
• Checkpointing: Support for task state persistence and resumption
• Dead Letter Queues: Failed tasks are preserved for analysis and manual intervention

Technical Questions

What queuing system does Eden Task use?
Eden Task is built on Redis Streams for high-performance queuing:

• High Throughput: Handles millions of messages per hour
• Persistence: Automatic message persistence and durability
• Scalability: Horizontal scaling with Redis clustering
• Low Latency: Sub-millisecond message delivery
• Consumer Groups: Multiple worker groups for load distribution

How does resource allocation work?
Eden Task provides intelligent resource management:

# Resource specification
resources:
  requests:
    cpu: "2"
    memory: "4Gi"
    storage: "10Gi"
  limits:
    cpu: "4"
    memory: "8Gi"
    storage: "20Gi"
  preferences:
    node_type: "compute-optimized"
    spot_instances: true
    max_cost_per_hour: "0.50"

What container orchestrators are supported?
Eden Task integrates with major container platforms:

• Kubernetes: Native integration with Jobs, CronJobs, and custom resources
• Docker Swarm: Support for Docker Swarm services and tasks
• Docker Compose: Local development and testing environments
• Cloud Container Services: AWS ECS, Google Cloud Run, Azure Container Instances
• Serverless: AWS Lambda, Google Cloud Functions for lightweight tasks

How does Eden Task handle scaling?
Eden Task provides automatic scaling capabilities:

• Queue-Based Scaling: Scale workers based on queue depth and task complexity
• Resource-Based Scaling: Scale based on CPU, memory, and custom metrics
• Predictive Scaling: ML-driven scaling based on historical patterns
• Cost-Optimized Scaling: Balance performance with cost constraints
• Multi-Cloud Scaling: Scale across different cloud providers for optimal pricing

Integration Questions

How do I submit tasks to Eden Task?
Tasks can be submitted through multiple interfaces:

# CLI submission
eden task submit ml-training.yaml --priority high --timeout 2h

# API submission
curl -X POST https://eden.company.com/api/tasks \
  -H "Authorization: Bearer $EDEN_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "data-processing",
    "image": "python:3.9",
    "command": ["python", "process.py"],
    "resources": {"cpu": "2", "memory": "4Gi"}
  }'

# SDK submission (Python)
from eden_task import TaskClient

client = TaskClient(token=os.environ['EDEN_TOKEN'])
task = client.submit_task(
    name="ml-training",
    image="tensorflow/tensorflow:latest",
    command=["python", "train.py"],
    resources={"cpu": "4", "memory": "16Gi", "gpu": "1"}
)

Can Eden Task integrate with existing CI/CD pipelines?
Yes, Eden Task provides seamless CI/CD integration:

• GitHub Actions: Custom actions for task submission and monitoring
• Jenkins: Plugin for Jenkins pipeline integration
• GitLab CI: Custom runners and pipeline integration
• Azure DevOps: Extension for Azure Pipelines
• Generic Webhooks: HTTP endpoints for any CI/CD system

How does Eden Task work with data storage?
Eden Task supports various data access patterns:

# Task with data volumes
name: "data-analysis"
image: "python:3.9"
command: ["python", "analyze.py"]
volumes:
  - name: "input-data"
    source: "s3://company-data/raw/"
    mount: "/data/input"
  - name: "output-data"
    source: "s3://company-data/processed/"
    mount: "/data/output"
  - name: "temp-storage"
    type: "ephemeral"
    size: "100Gi"
    mount: "/tmp"

Can I monitor task execution in real-time?
Yes, Eden Task provides comprehensive monitoring:

• Real-time Dashboards: Live task execution status and metrics
• Log Streaming: Real-time log aggregation and viewing
• Metrics Export: Prometheus-compatible metrics for custom dashboards
• Alerting: Integration with PagerDuty, Slack, and other notification systems
• Performance Analytics: Historical analysis and trend identification

Operational Questions

How do I handle long-running tasks?
Eden Task supports long-running operations:

# Long-running task configuration
name: "model-training"
timeout: "24h"
checkpoint:
  enabled: true
  interval: "1h"
  storage: "s3://checkpoints/model-training/"
health_check:
  command: ["python", "health_check.py"]
  interval: "5m"
  timeout: "30s"
resources:
  preemptible: false  # Use non-preemptible instances

What happens when tasks fail?
Eden Task provides comprehensive failure handling:

• Automatic Retries: Configurable retry policies with backoff strategies
• Failure Analysis: Detailed error reporting and root cause analysis
• Dead Letter Queues: Failed tasks preserved for manual investigation
• Rollback Capabilities: Ability to rollback to previous successful state
• Notification: Immediate alerts for critical task failures

How do I optimize task performance?
Eden Task includes performance optimization features:

• Resource Profiling: Automatic profiling of task resource usage
• Performance Recommendations: ML-driven suggestions for optimization
• Cost Analysis: Detailed cost breakdown and optimization opportunities
• Batch Optimization: Intelligent batching of similar tasks
• Cache Management: Automatic caching of frequently used data and images

Can I schedule recurring tasks?
Yes, Eden Task supports various scheduling patterns:

# Scheduled task
name: "daily-backup"
schedule:
  cron: "0 2 * * *"  # Daily at 2 AM
  timezone: "UTC"
image: "backup-tool:latest"
command: ["backup", "--full"]
resources:
  cpu: "1"
  memory: "2Gi"
retry:
  attempts: 3
  delay: "10m"

Key Features

Task Architecture

Eden Task’s architecture ensures high-performance, reliable, and scalable task orchestration:

┌─────────────────────────────────────────────────────────────────┐
│                    Task Submission Layer                        │
│  ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐│
│  │     CLI     │ │   REST API  │ │     SDK     │ │   Webhooks  ││
│  │  Interface  │ │  Endpoints  │ │ Libraries   │ │ Integration ││
│  └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘│
├─────────────────────────────────────────────────────────────────┤
│                   Orchestration Engine                         │
│  ┌─────────────────────────────────────────────────────────────┐│
│  │  Scheduling • Dependency Resolution • Resource Allocation  ││
│  └─────────────────────────────────────────────────────────────┘│
├─────────────────────────────────────────────────────────────────┤
│                     Queue Management                           │
│  ┌─────────────────────────────────────────────────────────────┐│
│  │    Redis Streams • Priority Queues • Dead Letter Queues    ││
│  └─────────────────────────────────────────────────────────────┘│
├─────────────────────────────────────────────────────────────────┤
│                    Execution Environment                       │
│  ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐│
│  │ Kubernetes  │ │   Docker    │ │   Cloud     │ │ Serverless  ││
│  │    Jobs     │ │   Swarm     │ │ Containers  │ │ Functions   ││
│  └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘│
├─────────────────────────────────────────────────────────────────┤
│                  Monitoring & Analytics                        │
│  ┌─────────────────────────────────────────────────────────────┐│
│  │   Metrics Collection • Performance Analysis • Alerting     ││
│  └─────────────────────────────────────────────────────────────┘│
└─────────────────────────────────────────────────────────────────┘

Use Cases

Getting Started

Basic Task Submission

# simple-task.yaml
name: "data-processing"
description: "Process daily data files"

image: "python:3.9"
command: ["python", "process_data.py"]
args: ["--input", "/data/raw", "--output", "/data/processed"]

resources:
  requests:
    cpu: "2"
    memory: "4Gi"
    storage: "10Gi"
  limits:
    cpu: "4"
    memory: "8Gi"

volumes:
  - name: "data-volume"
    source: "s3://company-data/"
    mount: "/data"

environment:
  - name: "DATABASE_URL"
    valueFrom:
      secretKeyRef:
        name: "db-credentials"
        key: "url"

timeout: "2h"
retry:
  attempts: 3
  delay: "5m"
  backoff: "exponential"

Complex Task Pipeline

# ml-pipeline.yaml
name: "ML Training Pipeline"
description: "Complete machine learning training workflow"

tasks:
  - name: "data-validation"
    image: "data-validator:latest"
    command: ["validate", "--schema", "/config/schema.json"]
    resources:
      cpu: "1"
      memory: "2Gi"
    volumes:
      - name: "raw-data"
        source: "s3://ml-data/raw/"
        mount: "/data"
    
  - name: "data-preprocessing"
    image: "python:3.9"
    command: ["python", "preprocess.py"]
    depends_on: ["data-validation"]
    resources:
      cpu: "4"
      memory: "8Gi"
    volumes:
      - name: "raw-data"
        source: "s3://ml-data/raw/"
        mount: "/data/raw"
      - name: "processed-data"
        source: "s3://ml-data/processed/"
        mount: "/data/processed"
    
  - name: "feature-engineering"
    image: "feature-engine:latest"
    command: ["engineer", "--config", "/config/features.yaml"]
    depends_on: ["data-preprocessing"]
    resources:
      cpu: "8"
      memory: "16Gi"
    parallel: 4  # Run 4 parallel feature engineering tasks
    
  - name: "model-training"
    image: "tensorflow/tensorflow:latest-gpu"
    command: ["python", "train.py"]
    depends_on: ["feature-engineering"]
    resources:
      cpu: "16"
      memory: "64Gi"
      gpu: "4"
    timeout: "12h"
    checkpoint:
      enabled: true
      interval: "30m"
      storage: "s3://ml-checkpoints/"
    
  - name: "model-evaluation"
    image: "python:3.9"
    command: ["python", "evaluate.py"]
    depends_on: ["model-training"]
    resources:
      cpu: "4"
      memory: "8Gi"
    
  - name: "model-deployment"
    image: "model-deployer:latest"
    command: ["deploy", "--model-path", "/models/latest"]
    depends_on: ["model-evaluation"]
    condition: "{{ tasks['model-evaluation'].result.accuracy > 0.95 }}"
    resources:
      cpu: "2"
      memory: "4Gi"

on_success:
  - name: "success-notification"
    action: "webhook"
    url: "https://api.company.com/ml-pipeline/success"
    
on_failure:
  - name: "failure-notification"
    action: "slack"
    channel: "#ml-team"
    message: "ML pipeline failed: {{ error.message }}"

CLI Usage

# Submit tasks
eden task submit simple-task.yaml
eden task submit ml-pipeline.yaml --priority high

# Monitor tasks
eden task list --status running
eden task status task-12345
eden task logs task-12345 --follow

# Manage task queues
eden task queue status
eden task queue pause ml-training
eden task queue resume ml-training

# Resource management
eden task resources --show-utilization
eden task scale workers --count 10 --node-type compute-optimized

# Scheduled tasks
eden task schedule daily-backup.yaml --cron "0 2 * * *"
eden task schedule list

SDK Integration

# Python SDK example
from eden_task import TaskClient, Task, Resources

client = TaskClient(
    api_url="https://eden.company.com",
    token=os.environ['EDEN_TOKEN']
)

# Submit a simple task
task = Task(
    name="data-processing",
    image="python:3.9",
    command=["python", "process.py"],
    resources=Resources(
        cpu="2",
        memory="4Gi",
        storage="10Gi"
    )
)

result = client.submit(task)
print(f"Task submitted: {result.task_id}")

# Wait for completion
status = client.wait_for_completion(result.task_id, timeout=3600)
if status.success:
    print("Task completed successfully")
    print(f"Output: {client.get_logs(result.task_id)}")
else:
    print(f"Task failed: {status.error}")

Kubernetes Integration

# Custom Resource Definition for Eden Tasks
apiVersion: eden.ataiva.com/v1
kind: EdenTask
metadata:
  name: ml-training
  namespace: ml-workloads
spec:
  image: tensorflow/tensorflow:latest-gpu
  command: ["python", "train.py"]
  resources:
    requests:
      cpu: "8"
      memory: "32Gi"
      nvidia.com/gpu: "2"
    limits:
      cpu: "16"
      memory: "64Gi"
      nvidia.com/gpu: "4"
  volumes:
    - name: training-data
      persistentVolumeClaim:
        claimName: ml-data-pvc
      mountPath: /data
  timeout: "6h"
  retryPolicy:
    attempts: 2
    delay: "10m"