Cortex Resource Manager

================================================================================ RESOURCE ALLOCATION IMPLEMENTATION - COMPLETE ================================================================================ Implementation Date: 2025-12-08 Location: /Users/ryandahlberg/Projects/resource-manager-mcp-server IMPLEMENTED TOOLS ================================================================================ 1. request_resources(job_id, mcp_servers, workers=None, priority="normal") - Reserve resources for a job - Start required MCP servers - Provision workers if requested - Track allocation with unique ID - Return allocation_id, status, endpoints, workers_allocated 2. release_resources(allocation_id) - Release resources after job completion - Scale down MCP servers (or mark for idle timeout) - Queue burst workers for destruction - Update allocation status 3. get_capacity() - Return current cluster capacity - Available CPU, memory, workers - Running MCP servers - Active allocations 4. get_allocation(allocation_id) - Get details of a specific allocation - Status, resources, age, job_id ADDITIONAL TOOLS ================================================================================ 5. list_allocations(state=None, job_id=None) - List allocations with optional filtering 6. cleanup_expired_allocations() - Manually trigger cleanup of expired allocations IMPLEMENTATION DETAILS ================================================================================ Core Module: src/allocation_manager.py (585 lines) - AllocationManager class - In-memory allocation tracking (dict/dataclass) - Allocation states: pending, active, releasing, released, failed - Priority levels: low, normal, high, critical - TTL/expiry handling - Capacity accounting Data Structures: - ResourceAllocation: Complete allocation record - ClusterCapacity: Cluster resource tracking - WorkerSpec: Worker instance specification - MCPServerSpec: MCP server specification - AllocationState enum - Priority enum MCP Server Integration: src/server.py - 6 MCP tools exposed - Background cleanup task (every 5 minutes) - Automatic expiry handling VALIDATION ================================================================================ Validation Results (validate.py): ✓ Created manager ✓ Initial capacity ✓ Requested resources (allocation_id=alloc-8e5073373de8) ✓ Capacity tracking ✓ Get allocation ✓ List allocations ✓ Released resources ✓ Capacity after release ✓ All tests passed! DOCUMENTATION ================================================================================ 1. RESOURCE_ALLOCATION.md - Comprehensive API reference - Usage examples - Best practices - Integration patterns - Error handling - Future enhancements 2. README.md (updated) - Added resource allocation section - Usage examples 3. IMPLEMENTATION_SUMMARY.md - Complete implementation details - File listing - Configuration - Integration points TESTS ================================================================================ tests/test_allocation_manager.py (28 tests): ✓ Initialization ✓ Resource requests (with/without workers) ✓ Capacity checking and tracking ✓ MCP server reuse ✓ Resource release ✓ Allocation queries ✓ TTL/expiry ✓ Priority levels ✓ Multiple concurrent allocations ✓ Resource accounting accuracy ✓ Error handling ✓ Data structure validation EXAMPLES ================================================================================ examples/allocation_example.py (5 interactive examples): 1. Basic resource allocation 2. Multiple concurrent allocations 3. Capacity checking 4. Allocation lifecycle 5. TTL expiry and cleanup Usage: python examples/allocation_example.py # Run all examples python examples/allocation_example.py 1 # Run example 1 USAGE ================================================================================ Direct Python API: from allocation_manager import AllocationManager manager = AllocationManager() allocation = manager.request_resources( job_id="job-001", mcp_servers=["filesystem", "github"], workers=4, priority="high" ) # Use resources... manager.release_resources(allocation['allocation_id']) Via MCP Server: python src/server.py # Then call via MCP protocol CONFIGURATION ================================================================================ Default Cluster Capacity: - CPU: 16 cores - Memory: 32GB (32768 MB) - Workers: 10 Default Worker Resources: - CPU: 1 core per worker - Memory: 2GB (2048 MB) per worker Port Allocation: - MCP servers: 9000-9099 - Workers: 8000+ INTEGRATION WITH CORTEX ================================================================================ Coordinator Master: - Requests resources for tasks - Passes allocation_id to worker masters Development Master: - Receives allocations - Uses MCP endpoints and workers - Releases on completion Security Master: - High-priority allocations - Short TTL for scans CI/CD Master: - Standard allocations - Handles deployments FEATURES ================================================================================ ✓ In-memory allocation tracking ✓ MCP server reuse across allocations ✓ Worker provisioning with unique IDs ✓ Capacity accounting (CPU, memory, workers) ✓ TTL/expiry handling ✓ Priority levels (4 levels) ✓ Metadata support ✓ Automatic cleanup ✓ Concurrent allocations ✓ Resource accounting accuracy FILES CREATED ================================================================================ Implementation: /Users/ryandahlberg/Projects/resource-manager-mcp-server/src/allocation_manager.py Documentation: /Users/ryandahlberg/Projects/resource-manager-mcp-server/RESOURCE_ALLOCATION.md /Users/ryandahlberg/Projects/resource-manager-mcp-server/IMPLEMENTATION_SUMMARY.md /Users/ryandahlberg/Projects/resource-manager-mcp-server/RESOURCE_ALLOCATION_COMPLETE.txt Tests: /Users/ryandahlberg/Projects/resource-manager-mcp-server/tests/test_allocation_manager.py Examples: /Users/ryandahlberg/Projects/resource-manager-mcp-server/examples/allocation_example.py Validation: /Users/ryandahlberg/Projects/resource-manager-mcp-server/validate.py NEXT STEPS ================================================================================ The resource allocation tools are complete and ready for integration with cortex. To use: 1. Start MCP server: python src/server.py 2. Call tools via MCP protocol or directly via AllocationManager 3. See RESOURCE_ALLOCATION.md for complete API reference 4. See examples/allocation_example.py for usage examples For production deployment: - Consider persistent storage (SQLite/PostgreSQL) - Add metrics and monitoring - Integrate with real Kubernetes worker provisioning - Implement health checks ================================================================================ IMPLEMENTATION STATUS: COMPLETE ✓ ================================================================================