bzzz/issues/resource-allocation-component.md

Resource Allocation Component Implementation

Overview

Implement a Resource Allocation component for the BZZZ setup wizard to allow users to configure CPU, memory, and storage allocation for different BZZZ services.

Current Status

• Step removed from setup wizard (temporarily) - located between AI Integration and Service Deployment
• Placeholder component exists at install/config-ui/app/setup/components/ResourceAllocation.tsx
• System detection available - hardware information already captured in System Detection step

Requirements

1. System Resource Display

• Display detected system resources from SystemDetection step:
  • CPU cores available
  • Total memory (RAM)
  • Available storage space
  • GPU information (if applicable)
• Show current system utilization/usage

2. Service Resource Configuration

• Allow allocation for BZZZ services:
  • BZZZ API Server (CPU cores, memory)
  • MCP Server (CPU cores, memory)
  • P2P Network (bandwidth, connections)
  • Web UI (minimal resources)
  • Storage Services (disk space allocation)
• Configurable resource limits and reservations
• Docker container resource constraints integration

3. Resource Validation

• Ensure total allocations don't exceed available resources
• Minimum resource requirements validation
• Warning for resource over-allocation
• Real-time calculation of remaining resources

4. Performance Recommendations

• Suggest optimal resource distributions based on:
  • Detected hardware capabilities
  • Intended cluster role (coordinator vs worker)
  • Expected workload patterns
• Preset configurations:
  • Development (minimal resources)
  • Production (balanced allocation)
  • High-Performance (maximum allocation)

5. Integration Points

• Receive system info from System Detection step
• Pass resource config to Service Deployment step
• Generate Docker Compose resource constraints
• Integration with cluster formation for multi-node awareness

Technical Implementation

Component Structure

interface ResourceConfig {
  cpu: {
    bzzzApi: number      // CPU cores for API server
    mcpServer: number    // CPU cores for MCP server
    p2pNetwork: number   // CPU cores for P2P
    reserved: number     // System reserved cores
  }
  memory: {
    bzzzApi: number      // Memory MB for API server
    mcpServer: number    // Memory MB for MCP server  
    p2pNetwork: number   // Memory MB for P2P
    webUI: number        // Memory MB for Web UI
    system: number       // System reserved memory
  }
  storage: {
    logs: number         // Log storage GB
    data: number         // Data storage GB
    temp: number         // Temporary files GB
    available: number    // Remaining space
  }
}

Validation Logic

const validateResources = (config: ResourceConfig, systemInfo: SystemInfo) => {
  const totalCpu = config.cpu.bzzzApi + config.cpu.mcpServer + config.cpu.p2pNetwork + config.cpu.reserved
  const totalMemory = config.memory.bzzzApi + config.memory.mcpServer + config.memory.p2pNetwork + config.memory.webUI + config.memory.system
  const totalStorage = config.storage.logs + config.storage.data + config.storage.temp
  
  return {
    cpuValid: totalCpu <= systemInfo.cpu_cores,
    memoryValid: totalMemory <= systemInfo.memory_mb,
    storageValid: totalStorage <= systemInfo.storage.free_space_gb
  }
}

User Experience

Visual Design

• Resource allocation sliders/input fields
• Real-time resource utilization charts
• Color-coded validation (green/yellow/red)
• Resource recommendation cards
• "Auto-Configure" button for presets

Workflow Integration

• Progressive disclosure (advanced options collapsed by default)
• Validation feedback before proceeding to next step
• Ability to return and modify allocations
• Save/restore configurations

Dependencies

• System Detection component (already implemented)
• Service Deployment component integration
• Docker Compose generation
• Cluster Formation multi-node awareness

Acceptance Criteria

• Users can allocate CPU, memory, and storage to BZZZ services
• Resource constraints are validated against available hardware
• Configuration is passed to subsequent setup steps
• Docker container limits are properly configured
• Performance recommendations are provided
• Multi-node cluster resource awareness

Priority

Medium - Important for production deployments but not blocking initial setup functionality

Related Issues

• Service Deployment component integration
• Docker Compose resource constraints
• Multi-node cluster resource coordination
• Performance monitoring and alerting

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Implementation Notes:

• Reintegrate into setup wizard between AI Integration and Service Deployment
• Use existing SystemInfo from setup context
• Generate Docker Compose resource configurations
• Consider future multi-node resource coordination requirements