Design_Doc.MD

Design Document

Overview

The Topology-Aware GPU Scheduler is a custom Kubernetes scheduler extension that optimizes GPU workload placement by considering network topology constraints. It is designed to improve performance for GPU-intensive workloads in high-performance computing clusters by up to 30% through intelligent placement decisions that respect physical network topology.

Goals

• Optimize GPU workload placement based on network topology
• Minimize inter-node communication overhead for multi-GPU workloads
• Support different placement strategies for various workload types
• Provide automatic recovery while maintaining topology constraints
• Prevent cluster fragmentation
• Maintain high scheduling performance (sub-500ms latency)

System Architecture

Components

1. Scheduler Core

   • Implements custom scheduling logic
   • Integrates with Kubernetes scheduler framework
   • Manages placement decisions based on topology constraints
   • Handles scheduling queue and prioritization

2. Domain Manager

   • Maintains network topology information
   • Tracks domain relationships (leaf-spine architecture)
   • Updates domain state in real-time
   • Handles domain capacity management

3. Plugin Framework

   • Filter plugins for constraint validation
   • Score plugins for placement optimization
   • Binding plugins for resource allocation
   • Extension points for custom logic

4. Metrics Collector

   • Gathers performance metrics
   • Monitors resource utilization
   • Tracks scheduling decisions
   • Exports Prometheus metrics

Data Model

type TopologyDomain struct {
    Name        string
    Type        DomainType  // Leaf or Spine
    Capacity    Resources
    Utilization Resources
    Nodes       []string
    Connected   []string    // Connected domains
}

type PlacementStrategy struct {
    Type           StrategyType
    WeightFactors  map[string]float64
    Constraints    []Constraint
}

type SchedulingDecision struct {
    JobID     string
    Domain    string
    NodeSet   []string
    Strategy  PlacementStrategy
    Score     float64
}

Core Algorithms

Placement Decision Flow

1. Job Analysis

   • Parse job requirements and annotations
   • Determine GPU count and constraints
   • Select appropriate placement strategy

2. Domain Selection

   • Filter eligible domains based on capacity
   • Score domains based on:
     • Resource availability (40%)
     • Topology alignment (30%)
     • Domain utilization (20%)
     • Historical performance (10%)

3. Node Selection

   • Filter nodes within selected domain
   • Apply anti-fragmentation rules
   • Consider hardware affinity

4. Placement Validation

   • Verify topology constraints
   • Check network bandwidth requirements
   • Validate domain capacity

Recovery Mechanism

1. Detect node/domain failures
2. Identify affected workloads
3. Calculate minimal migration set
4. Execute migrations while maintaining topology constraints

Performance Considerations

Latency Targets

• Scheduling decisions: < 500ms
• Recovery time: < 30s
• Domain state updates: < 100ms

Scalability

• Support for up to 1000 nodes
• Handle up to 10,000 GPU devices
• Process 100 scheduling decisions/second

Resource Efficiency

• Memory usage: < 512MB
• CPU usage: < 1 core under normal load
• Network overhead: < 1MB/s

Monitoring and Observability

Metrics

topology_scheduler_latency_seconds
topology_domain_utilization_ratio
topology_gpu_allocation_ratio
topology_placement_decisions_total
topology_recovery_duration_seconds

Logging

• Structured JSON logging
• Debug level for development
• Info level for production
• Error details for failures

Future Improvements

1. Short Term

   • Add support for custom topology rules
   • Implement placement strategy plugins
   • Enhance recovery mechanisms

2. Medium Term

   • Add machine learning-based placement optimization
   • Implement predictive scaling
   • Add support for custom network architectures

3. Long Term

   • Dynamic topology discovery
   • Multi-cluster support
   • Advanced failover strategies

Implementation Plan

Phase 1: Core Framework

• Basic scheduler implementation
• Domain management
• Simple placement strategies

Phase 2: Advanced Features

• Complex topology support
• Recovery mechanisms
• Metric collection

Phase 3: Optimization

• Performance tuning
• Advanced algorithms
• Production hardening

Security Considerations

1. Access Control

   • RBAC for scheduler operations
   • Namespace isolation
   • Domain access restrictions

2. Data Protection

   • Encryption of sensitive data
   • Secure communication channels
   • Audit logging

3. Operational Security

   • Resource quotas
   • Rate limiting
   • Failure isolation

References

1. Kubernetes Scheduler Framework
2. GPU Topology Best Practices
3. Network Architecture Guidelines
4. Performance Optimization Techniques