EffiScale: Orchestrated Elasticity Framework for Cloud-native Container Scaling

🌟 Overview

EffiScale is a sophisticated, self-adaptive framework for optimizing container scaling in cloud environments. Built on a microservices architecture, it provides intelligent resource management through comprehensive monitoring, analysis, and execution capabilities.

🏗️ Project Structure

• EffiScale/
  • src/
    • core/ (Core Framework Components)
      • config.py (Configuration Management)
      • exceptions.py (Custom Exceptions)
      • utils.py (Utility Functions)
    • monitoring/ (Monitoring System)
      • collector.py (Metric Collection)
      • metrics.py (Metric Definitions)
      • prometheus.py (Prometheus Integration)
    • analysis/ (Analysis Engine)
      • pattern_analyzer.py
      • threshold_manager.py
      • workload_predictor.py
      • resource_optimizer.py
    • controllers/ (Control System)
      • elastic_controller.py
      • decision_maker.py
      • consensus_manager.py
      • state_manager.py
    • knowledge/ (Knowledge Base)
      • knowledge_base.py
      • knowledge_validator.py
      • pattern_library.py
    • execution/ (Execution Engine)
      • scaling_executor.py
      • container_manager.py
      • rollback_manager.py
    • api/ (API Layer)
      • routes.py
      • models.py
      • validators.py
    • storage/ (Storage Layer)
      • metric_storage.py
      • database.py

🚀 Key Components

Core Framework (src/core/)

• Configuration management and system bootstrapping
• Exception handling and error management
• Utility functions and common operations

Monitoring System (src/monitoring/)

• Real-time metric collection with Prometheus integration
• Custom metric definitions and performance monitoring
• Resource utilization tracking and analysis

Analysis Engine (src/analysis/)

• Pattern analysis and detection algorithms
• Dynamic threshold management and adjustment
• Workload prediction and resource optimization

Control System (src/controllers/)

• Elastic scaling control mechanisms
• Intelligent decision making logic
• Consensus and state management

Knowledge Base (src/knowledge/)

• Pattern storage and retrieval systems
• Knowledge validation and verification
• Pattern library management and updates

Execution Engine (src/execution/)

• Scaling action execution and coordination
• Container lifecycle management
• Rollback mechanism implementation

API Layer (src/api/)

• RESTful API endpoints
• Data models and schemas
• Request/response validation

Storage Layer (src/storage/)

• Metric data persistence
• Database operations and management

🛠️ Installation

git clone https://github.com/ahmadpanah/EffiScale.git
pip install -r requirements.txt
docker-compose -f docker/docker-compose.yml build

📋 Basic Usage

from effiscale.core import EffiScale

# Initialize and start EffiScale
effiscale = EffiScale(config_path="config.yaml")
effiscale.start()

⚙️ Configuration Example

monitoring:
  collector:
    interval: 10
    metrics: [cpu_usage, memory_usage, network_io]
analysis:
  thresholds:
    cpu: 80
    memory: 75
  prediction:
    window: 300
    algorithm: "lstm"
execution:
  scaling:
    min_instances: 1
    max_instances: 10
    cooldown: 300

🚢 Docker Commands

docker-compose -f docker/docker-compose.yml up -d
docker-compose -f docker/docker-compose.yml ps
docker-compose -f docker/docker-compose.yml logs -f

🔌 API Endpoints

• Monitoring
  • GET /api/v1/metrics - Get system metrics
  • GET /api/v1/status - Get system status
• Control
  • POST /api/v1/scale - Trigger scaling
  • GET /api/v1/decisions - Get scaling decisions
• Analysis
  • GET /api/v1/patterns - Get scaling patterns
  • POST /api/v1/patterns - Create new pattern

📊 Key Features

• Intelligent Scaling
  • Predictive scaling capabilities
  • Pattern-based decision making
  • Resource optimization
  • Workload analysis
• High Availability
  • Fault tolerance mechanisms
  • Automatic recovery procedures
  • State persistence
  • Rollback capabilities
• Monitoring & Analytics
  • Real-time metric tracking
  • Historical data analysis
  • Pattern recognition
  • Performance monitoring

📝 Documentation

• Implementation guides: /examples

🤝 Contributing

1. Fork repository
2. Create feature branch
3. Commit changes
4. Push to branch
5. Create Pull Request

📄 License

MIT License - see LICENSE

🔄 Roadmap

• Custom Metric Support
• Multi-cluster Management
• Advanced Analytics Dashboard
• Auto-tuning Capabilities

Contact

• Seyed Hossein Ahmadpanah - h.ahmadpanah@iau.ac.ir