yggdrasilArchitecture

Yggdrasil Architecture

Technical deep dive into the Yggdrasil distributed infrastructure system.

Overview

Yggdrasil is designed as a distributed, fault-tolerant infrastructure system that provides the foundation for all Polysystems AI services. This page explores the technical architecture, design decisions, and implementation details.

Coming Soon

Detailed architecture documentation is currently being prepared. This will include:

  • System Design: Architecture diagrams and component interactions
  • Core Components: Detailed specifications of each component
  • Communication Patterns: How services communicate within Yggdrasil
  • Data Management: State storage and synchronization
  • Scaling Mechanisms: How Yggdrasil achieves horizontal scalability
  • Fault Tolerance: Redundancy and failover strategies
  • Performance: Optimization techniques and benchmarks

High-Level Architecture

Documentation coming soon.

Core Components

Control Plane

The control plane manages the overall state and orchestration of the system:

  • Cluster Manager: Maintains cluster state and node membership
  • Scheduler: Decides where to place service instances
  • Service Registry: Tracks all registered services and their locations
  • Health Monitor: Continuously monitors service and node health
  • Configuration Store: Centralized configuration management

Data Plane

The data plane handles actual service traffic:

  • Service Mesh: Manages service-to-service communication
  • Load Balancer: Distributes traffic across service instances
  • Proxy Layer: Handles network traffic routing
  • Service Instances: Running AI services and applications

Storage Layer

Persistent storage for system state:

  • Distributed Key-Value Store: For configuration and state
  • Service Metadata: Information about deployed services
  • Logs & Metrics: Operational data storage
  • Checkpoint Storage: For service state persistence

Design Principles

Documentation coming soon.

Distributed by Design

Yggdrasil is built from the ground up as a distributed system with no single point of failure.

Service-Oriented

Everything in Yggdrasil is a service, making the system modular and extensible.

API-First

All operations are exposed through well-defined APIs for automation and integration.

Cloud-Native

Designed to run efficiently on cloud infrastructure with containerized workloads.

Service Orchestration

Documentation coming soon.

Service Lifecycle

Deploy → Schedule → Start → Run → Monitor → Scale/Update → Stop

Scheduling Algorithm

How Yggdrasil decides where to place services:

  • Resource availability
  • Load balancing
  • Affinity/anti-affinity rules
  • Geographic constraints
  • Cost optimization

Service Discovery

Automatic service registration and discovery mechanisms:

  • DNS-based discovery
  • Service registry lookup
  • Dynamic endpoint resolution

Networking

Documentation coming soon.

Service Mesh

Yggdrasil includes a built-in service mesh for:

  • Secure service-to-service communication
  • Traffic management and routing
  • Load balancing
  • Circuit breaking
  • Retry logic

Network Isolation

  • Virtual network segmentation
  • Network policies
  • Security groups
  • Traffic encryption

Scalability

Documentation coming soon.

Horizontal Scaling

  • Automatic pod scaling based on metrics
  • Manual scaling controls
  • Scale-to-zero capabilities
  • Burst scaling for sudden demand

Vertical Scaling

  • Resource limit adjustments
  • Dynamic resource allocation
  • Resource quotas

Cluster Scaling

  • Node auto-scaling
  • Multi-region support
  • Cross-cluster communication

High Availability

Documentation coming soon.

Redundancy

  • Multi-instance deployment
  • Geographic distribution
  • Backup control plane components

Failover

  • Automatic failover detection
  • Service restart policies
  • Health-based routing
  • Graceful degradation

Data Replication

  • State synchronization
  • Consensus algorithms
  • Eventual consistency

Performance

Documentation coming soon.

Optimization Techniques

  • Request caching
  • Connection pooling
  • Batch processing
  • Asynchronous operations

Resource Management

  • CPU and memory limits
  • GPU allocation
  • Storage I/O optimization
  • Network bandwidth management

Monitoring Metrics

  • Latency (p50, p95, p99)
  • Throughput (requests/second)
  • Error rates
  • Resource utilization

Security

Documentation coming soon.

Authentication & Authorization

  • mTLS for service communication
  • API key validation
  • Role-based access control (RBAC)
  • Service accounts

Network Security

  • Network policies
  • Firewall rules
  • DDoS protection
  • Traffic encryption

Secrets Management

  • Encrypted secrets storage
  • Secret rotation
  • Access control
  • Audit logging

Observability

Documentation coming soon.

Logging

  • Centralized log aggregation
  • Structured logging
  • Log retention policies
  • Log search and analysis

Metrics

  • System metrics (CPU, memory, disk, network)
  • Application metrics (custom metrics)
  • Service-level indicators (SLIs)
  • Service-level objectives (SLOs)

Tracing

  • Distributed tracing
  • Request flow visualization
  • Performance bottleneck identification

Alerting

  • Metric-based alerts
  • Log-based alerts
  • Alert routing and escalation
  • On-call integration

Deployment Models

Documentation coming soon.

Single Cluster

For development and small-scale deployments.

Multi-Cluster

For high availability and geographic distribution.

Hybrid Cloud

Spanning multiple cloud providers or on-premises infrastructure.

Integration Points

Documentation coming soon.

Hub Integration

How Yggdrasil integrates with the Polysystems Hub service for request routing.

OMM Integration

Running OMM services on Yggdrasil infrastructure.

Payment Integration

Resource tracking for billing and credit management.

Technical Specifications

Documentation coming soon.

Supported Workloads

  • Containerized applications
  • ML model serving
  • Batch processing jobs
  • Streaming data processing

Resource Types

  • Compute (CPU, GPU)
  • Memory (RAM)
  • Storage (persistent volumes)
  • Network (bandwidth, connections)

Comparison with Other Systems

Documentation coming soon.

Yggdrasil compared to:

  • Kubernetes
  • Docker Swarm
  • Apache Mesos
  • Nomad

Future Roadmap

Documentation coming soon.

Planned features and improvements:

  • Enhanced GPU support
  • Serverless function execution
  • Edge computing support
  • Advanced scheduling algorithms

Related Documentation

Contributing

Information about contributing to Yggdrasil development (coming soon).

Note: This documentation is actively being developed. For the most up-to-date technical information, please contact our engineering team or check back regularly for updates.

Getting Started