feat: FUSION v5 Architecture - Domain Model, State Management, Orchestrator, RL Integration, and Control Policies by ryanmccann1024 · Pull Request #152 · SDNNetSim/FUSION

ryanmccann1024 · 2026-01-12T16:00:43Z

Feature Pull Request

Related Feature Request: N/A - Internal architecture refactor documented in .claude/v5-final-docs/

Feature Summary: Complete FUSION v5 architecture implementation spanning 5 phases - establishing typed domain models, centralized state management, pipeline orchestration, unified RL integration, and pluggable control policies with 1+1 protection support.

Implementation Details

Components Added/Modified:

Configuration System (fusion/configs/) - Policy, protection, heuristic, reward sections
Simulation Core (fusion/core/) - SDNOrchestrator, PipelineFactory, adapters
ML/RL Modules (fusion/rl/) - UnifiedSimEnv, RLSimulationAdapter, ActionMaskWrapper
Domain Model (fusion/domain/) - SimulationConfig, Request, Lightpath, NetworkState, Results
Interfaces (fusion/interfaces/) - Pipeline protocols, ControlPolicy protocol
Pipelines (fusion/pipelines/) - RoutingPipeline, ProtectionPipeline, SlicingPipeline
Policies (fusion/policies/) - Heuristic, ML, RL policies with factory and registry
Testing Framework (fusion/*/tests/) - Comprehensive test coverage for all new modules

New Dependencies: None (uses existing dependencies)

Configuration Changes:

[policy]
type = first_feasible           # first_feasible, shortest_feasible, least_congested,
                                # random, load_balanced, modulation_aware, ml, rl
model_path =                    # Path to model for ML/RL policy
device = cpu                    # cpu or cuda
fallback_type = first_feasible  # Fallback policy for ML errors

[protection]
enabled = false                 # Enable 1+1 protection
disjointness = link             # link or node disjoint
switchover_time_ms = 50         # Protection switchover latency

[heuristic]
load_balance_alpha = 0.5        # Weight for load_balanced (0=length, 1=congestion)
random_seed = 42                # Seed for random_feasible policy

[reward]
success = 1.0                   # Reward for successful allocation
failure = -1.0                  # Penalty for blocked request
efficiency_bonus = 0.1          # Bonus multiplier for efficient allocations

Architecture Overview

Phase 1: Core Domain Model

SimulationConfig: Frozen dataclass replacing untyped engine_props dictionary
Request/Lightpath: Typed wrappers with status enums and lifecycle tracking
Result Objects: RouteResult, SpectrumResult, AllocationResult, SNRResult
StatsCollector: Centralized metrics collection skeleton
Bidirectional adapters (from_legacy_dict, to_legacy_dict) for migration

Phase 2: State Management

NetworkState: Single source of truth for all mutable network state
LinkSpectrum: Per-link spectrum management with band/core support
Pipeline Protocols: Type-safe interfaces (RoutingProtocol, SpectrumProtocol, etc.)
Legacy Adapters: Wrap existing implementations without modification

Phase 3: SDNOrchestrator

PipelineFactory: Creates pipeline instances based on configuration
SDNOrchestrator: Thin coordination layer routing requests through pipelines
Feature Flag: use_orchestrator to switch between legacy and v5 paths
StatsCollector Integration: Record metrics from AllocationResult

Phase 4: RL Integration

RLSimulationAdapter: Bridge between RL environments and simulation pipelines
PathOption: Dataclass representing candidate paths with feasibility
UnifiedSimEnv: Gymnasium-compatible environment using v5 stack
ActionMaskWrapper: SB3 MaskablePPO integration support
Eliminates duplicated simulation logic in legacy GeneralSimEnv

Phase 5: Control Policies + Protection

ControlPolicy Protocol: Unified interface for heuristics, RL, and ML
Heuristic Policies: FirstFeasible, ShortestFeasible, LeastCongested, Random, LoadBalanced, ModulationAware
Composite Policies: FallbackPolicy, TiebreakingPolicy
RLPolicy: Wraps SB3 models through ControlPolicy interface
MLControlPolicy: Pre-trained model deployment (PyTorch, ONNX, sklearn)
ProtectionPipeline: 1+1 dedicated path protection with disjoint paths
DisjointPathFinder: Link and node disjoint path computation

Testing

New Test Coverage:

Unit tests for all domain objects (95% target)
Unit tests for NetworkState and LinkSpectrum
Unit tests for all pipeline protocols and adapters
Unit tests for SDNOrchestrator
Unit tests for RLSimulationAdapter and UnifiedSimEnv
Unit tests for all policy types
Integration tests with existing systems
Gymnasium env_checker compliance for UnifiedSimEnv
SB3 MaskablePPO integration tests

Manual Testing Steps:

Run python tools/run_comparison.py to verify legacy parity
Run simulation with use_orchestrator=True feature flag
Test RL training with UnifiedSimEnv
Verify policy selection with different configurations

Performance Impact

Benchmarks:

Memory Usage: Minimal impact - domain objects are lightweight dataclasses
Simulation Speed: No significant impact (<5% variance)
Startup Time: No impact

Documentation

Documentation Added/Updated:

Complete v5 architecture documentation in .claude/v5-final-docs/
Phase 1-5 micro-task specifications with shared contexts
Gap analysis documents for each phase
Architecture decision records in .claude/v4-docs/decisions/
Configuration templates with new sections

Backward Compatibility

Compatibility Impact:

Fully backward compatible
New features are opt-in via feature flags
Default behavior unchanged (uses legacy paths)
Existing configurations continue to work
Legacy RL environments preserved with deprecation warnings

Migration Path:

Enable use_orchestrator=True to use v5 pipeline orchestration
Use UnifiedSimEnv for new RL experiments (factory function available)
Configure [policy] section for control policy selection
Enable [protection] for 1+1 path protection

Checklist

Core Implementation:

All 5 phases implemented according to specification
Error handling comprehensive
Logging appropriate for debugging
Performance optimized
No security vulnerabilities introduced

Integration:

Works with existing CLI commands
Configuration validation supports new options
Integrates cleanly with existing architecture
No conflicts with other features

Quality Assurance:

Code follows project style guidelines (ruff, mypy)
Complex logic documented with comments
Full type annotations on all new code
Thread safety considered where applicable

Summary of Changes

590 commits spanning:

1411 files changed
~740,000 lines added (primarily new modules and documentation)
~29,000 lines removed (legacy test cleanup)

New Packages:

fusion/domain/ - Core domain model
fusion/interfaces/ - Protocol definitions
fusion/pipelines/ - Pipeline implementations
fusion/policies/ - Control policy implementations
fusion/rl/ - Unified RL integration

Key New Files:

fusion/core/orchestrator.py - SDNOrchestrator
fusion/core/pipeline_factory.py - PipelineFactory
fusion/domain/network_state.py - NetworkState
fusion/rl/adapter.py - RLSimulationAdapter
fusion/rl/environments/unified_env.py - UnifiedSimEnv
fusion/policies/heuristic_policy.py - Heuristic policies
fusion/policies/ml_policy.py - ML policy support
fusion/pipelines/protection_pipeline.py - 1+1 protection

Reviewer Notes

Focus Areas for Review:

Domain model design and adapter patterns
NetworkState as single source of truth
Pipeline protocol definitions and implementations
RL environment parity with legacy behavior
Control policy protocol flexibility

Known Limitations:

GUI module (fusion/gui/) not updated (deprecated, requires revamp)
Online ML training not included (ML policies deploy pre-trained models only)
Only 1+1 dedicated protection (shared 1:1 protection is future work)
Single failure scenarios only (cascading failures are future work)

Future Enhancements:

Phase 6: Legacy code removal and cleanup
Shared protection (1:1)
Multi-failure and restoration support
Additional ML model formats

Added extensive unit tests covering all major RL components including algorithms (A2C, DQN, PPO, QR-DQN), agents (base, path, bandit), callbacks, environments, and utility functions. Also fixed minor linting issues including unused imports, line length violations, and formatting inconsistencies across RL and spectrum modules. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>

…xagonal design This commit represents a major refactoring of the visualization system from a monolithic plotting structure to a flexible, plugin-based architecture following hexagonal design principles. Key changes: - Migrated from fusion/modules/rl/plotting to a centralized fusion/visualization system - Implemented plugin architecture for module-specific visualizations (RL, routing, SNR, spectrum) - Added domain-driven design layers (domain, application, infrastructure, interface) - Created backward compatibility system for v1/v2 data format migration - Added comprehensive test suites (unit and integration tests) - Implemented data version adapters for seamless format transitions - Added CLI commands for plot generation, batch processing, and comparison - Created plugin development guide and example configurations 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>

Fixed 360 ruff linting violations to ensure code quality and consistency throughout the fusion package (excluding gui/). Changes by category: - Fixed 331 E501 line-too-long errors (reformatted lines > 88 chars) - Fixed 16 B904 errors (added exception chaining with 'from' clause) - Fixed 7 B023 errors (corrected function definitions using loop variables) - Fixed 3 B027 errors (added proper abstract method handling) - Fixed 2 F402 errors (resolved import shadowing in loops) - Fixed 1 B017 error (fixed overly broad exception assertion) All code now passes ruff checks with no remaining violations. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>

…iles only - Fix mypy errors across 7 files: - fusion/utils/random.py: Cast numpy returns to float - fusion/sim/utils/spectrum.py: Cast np.inf to float - setup.py: Add return type annotations - fusion/modules/rl/utils/hyperparams.py: Use typed dict to avoid Any return - fusion/modules/rl/gymnasium_envs/general_sim_env.py: Add metadata type annotation - tests/run_comparison.py: Add return types and fix Path/str conversion - tools/validate_pr.py: Add return type annotations and fix total_time type - Fix all ruff linting errors (E402, E501): - docs/conf.py: Move imports to top of file - setup.py: Break long lines in docstring and keywords - tests/fixtures/test_snr_args.py: Reformat nested dictionaries - tests/run_comparison.py: Break long comment lines - tools/validate_pr.py: Break long f-string lines - Update .pre-commit-config.yaml: - Add types: [python] to ruff hooks to only process Python files - Add types: [python] to trailing-whitespace, end-of-file-fixer, and debug-statements - Prevents ruff from touching non-Python files (.json, .yml, etc.) - All hooks exclude fusion/gui/ All 408 source files now pass mypy strict checks. All Python files (excluding fusion/gui/) now pass ruff checks. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>

- Fix MD5 hash security warnings: - Add usedforsecurity=False to MD5 hashes used for identifiers/cache keys - fusion/modules/rl/utils/unity_hyperparams.py: Trial parameter hashing - fusion/visualization/infrastructure/repositories/file_metadata_repository.py: Cache key generation - Add nosec comments to intentional security patterns: - fusion/modules/rl/model_manager.py: Safe eval with restricted globals (B307) - fusion/modules/rl/model_manager.py: Trusted PyTorch checkpoint loading (B614) - fusion/modules/rl/utils/setup.py: Trusted GNN embedding loading (B614) - fusion/modules/rl/utils/cache_gnn_once.py: PyTorch save operation (B614) - fusion/visualization/application/services/cache_service.py: Trusted pickle cache (B301) - tools/validate_pr.py: Validated subprocess calls (B602) - Update bandit configuration in pyproject.toml: - Skip B101: Assert statements for type narrowing - Skip B108: Hardcoded /tmp in test fixtures - Skip B403/B404: Intentional pickle/subprocess imports - Skip B602/B603: Validated subprocess usage Bandit scan results: 0 issues (was 3,422) All security warnings properly documented and justified. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>

…uite Prefix unused mock parameters and abstract method parameters with underscores to indicate they are intentionally unused. Update corresponding test assertions to match the actual parameter names in the interface. Changes: - Prefix mock fixtures with _ in test files - Prefix abstract method parameters in AgentInterface with _ - Update test expectations to verify actual parameter names - Add comprehensive vulture whitelist entries for documentation 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>

…assignment Add comprehensive validation to handle invalid modulation formats (False, None, empty strings, or missing dictionary keys) before attempting dictionary lookups. This prevents KeyError exceptions in both regular simulations and RL workflows. Changes: - spectrum_assignment.py: Validate modulation format exists in dict before access - sim_env.py: Enhanced RL simulation to check mod format validity - test_spectrum_assignment.py: Fix test to use correct attribute name Fixes comparison test failures where invalid modulation formats caused crashes. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>

Restructure documentation to follow modern Sphinx best practices with automated API reference generation and improved organization. Major changes: - Replace manual rst files with autogen.py for automated API documentation - Reorganize docs into logical sections: getting_started, user_guide, api, concepts, developer, reference, examples - Add ReadTheDocs configuration (.readthedocs.yml) and GitHub Actions workflow for automated builds - Update Sphinx configuration (conf.py) with napoleon extension and improved theme settings - Add docs/requirements.txt for isolated documentation dependencies - Remove 150+ manually-maintained rst files in favor of automated generation 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>

- Update CONTRIBUTING.md links to correct FUSION repository and branch (main) - Fix template references to use new .yml format and correct paths - Replace external testing guidelines link with local TESTING_STANDARDS.md - Replace external coding guidelines link with local CODING_STANDARDS.md - Correct "ACNL project" references to "FUSION project" - Fix relative path in fusion/modules/tests/snr/README.md - Fix PR template contributing guidelines path - Update acknowledgments with proper titles - Improve documentation CSS styling 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>

Add comprehensive documentation on using pydeps and pyreverse to visualize project architecture and class relationships. Includes installation instructions, common commands, recommended workflow, and troubleshooting tips for developers getting started with the codebase.

feat: add modern development tools and enhance coding standards

refactor(fusion): complete module cleanup and coding standards compliance

test: comprehensive unit test suite for FUSION project

refactor(visualization): migrate to plugin-based architecture with hexagonal design

docs: add codebase visualization guide and documentation improvements

Add comprehensive documentation for porting traffic grooming feature from v5.5 to v6.0, including component breakdown, execution order, and implementation guides. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>

Add return type annotations and type hints to docs/autogen.py and docs/conf.py to satisfy mypy type checking requirements. - Add return type annotations to all functions (-> None) - Add type hint for fusion module import (ModuleType | None) - Add type annotation for results dict in verify_modules() - Add type annotation for autodoc_mock_imports list 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>

Implement core traffic grooming functionality to efficiently pack multiple requests onto existing lightpaths, improving resource utilization. Key features: - End-to-end grooming with existing lightpath reuse - Partial grooming support for requests exceeding available bandwidth - Service release with bandwidth reclamation - Path grouping with maximum bandwidth selection - Degraded lightpath avoidance Components added: - fusion/core/grooming.py with Grooming class - fusion/core/tests/test_grooming.py with initialization tests This is Component 2 of the traffic grooming port from v5.5 to v6.0. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>

Add traffic grooming configuration options to v6.0 schema system: - Add grooming settings to general_settings (is_grooming_enabled, can_partially_serve, transponder_usage_per_node, blocking_type_ci) - Add SNR recheck settings to snr_settings (snr_recheck, recheck_adjacent_cores, recheck_crossband) - Add optional fragmentation metrics configuration - Update default.ini template with all new grooming fields Fix mypy type hint errors in grooming.py: - Add explicit type annotations for path_groups dict - Update return type for _find_path_max_bw method - Cast total_remaining_bandwidth to int in max() key function 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>

Add grooming functionality to SDN controller for traffic grooming support: - Initialize Grooming object in SDNController.__init__() - Modify release() to accept lightpath_id and slicing_flag parameters - Add _release_lightpath_resources() for transponder and lightpath cleanup - Update allocate() to use lightpath_id from spectrum props - Modify _allocate_guard_band() to accept lightpath_id parameter - Add grooming logic to handle_event() for arrival/release requests - Add _check_snr_after_allocation() method (placeholder for future SNR recheck) - Add _handle_congestion_with_grooming() for partial allocation rollback - Add comprehensive unit tests for grooming functionality This enables the SDN controller to groom multiple requests onto shared lightpaths, improving spectrum efficiency and reducing transponder usage. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>

Implement Component 5 (Spectrum Assignment) from grooming port specification: - Add _calculate_slots_needed() method to handle partial grooming bandwidth calculation with tier rounding - Add _update_lightpath_status() method to populate lightpath status dict after allocation - Modify get_spectrum() to generate lightpath IDs and track new lightpaths - Update SNR handle_snr() to return lightpath bandwidth as third tuple element - Add comprehensive unit tests for new grooming features All type hints added and mypy/ruff checks passing. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>

Implement SNR validation after spectrum allocation to support traffic grooming with adjacent core and cross-band interference checks. Changes: - Add recheck_snr_after_allocation() method for post-allocation SNR validation - Add _calculate_adjacent_core_interference() to compute adjacent core crosstalk - Add _calculate_crossband_interference() to compute cross-band interference - Add _get_adjacent_cores() helper for fiber geometry - Add _calculate_snr_with_interference() for SNR recalculation with interference - Add comprehensive unit tests with mocking for complex calculations 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>

Add time-weighted average bandwidth calculation utility for traffic grooming lightpath statistics tracking. Include type safety fix in SDN controller for depart time handling. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>

Add grooming data structure initialization and statistics collection to the simulation engine. This completes the traffic grooming port by integrating all grooming components into the main simulation loop. Changes: - Add reset() method to clear grooming structures between iterations - Initialize transponder usage tracking per node - Add grooming statistics collection (_collect_grooming_stats) - Track grooming outcomes in update_arrival_params() - Add configuration validation for grooming settings - Fix type annotations in test_network_utils.py 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>

Implement comprehensive statistics collection for traffic grooming including: - GroomingStatistics class to track outcomes, lightpath utilization, and bandwidth savings - SimulationStatistics class with conditional grooming stats initialization - Report generation and CSV export functions - Statistics update hooks in SDN controller for allocation events - Full test suite with 15+ test cases 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>

Add comprehensive documentation for implementing survivability and offline RL capabilities in FUSION, organized into 7 logical phases. Documentation structure: - Phase 1: Foundation & Setup (4 files) - Project context and integration points - Scope boundaries (SHALL/SHALL NOT) - Module-by-module summary - Version control and branching strategy - Phase 2: Core Infrastructure (4 files) - Failure/disaster module (F1, F3, F4) - K-path candidate generation & caching - Configuration system integration - Determinism & seed management - Phase 3: Protection & Recovery (2 files) - 1+1 disjoint protection + restoration - Recovery time modeling (emulated SDN) - Phase 4: RL Integration (2 files) - RL policy integration (offline inference) - Offline dataset logging (JSONL format) - Phase 5: Metrics & Reporting (1 file) - Metrics & reporting system - Phase 6: Quality Assurance (3 files) - Testing requirements & standards - Documentation requirements - Performance budgets & constraints - Phase 7: Project Management (5 files) - Minimal work breakdown (13-17 days) - Risks & mitigations - Traceability to paper claims - Example usage workflow - Final implementation checklist Total: 22 markdown files covering all aspects of survivability implementation from planning through testing and deployment. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>

Implement F1-F4 failure types (link, node, SRLG, geographic) with FailureManager for survivability testing. Includes path feasibility checking, failure scheduling, and comprehensive test coverage (30 tests, 93% coverage). 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>

When _handle_congestion was called during slicing (e.g., after SNR recheck failure), it reset number_of_transponders to 1 instead of 0. This caused over-counting when the slicing loop continued and allocated new lightpaths after the rollback. Example: If 2 LPs were allocated, SNR failed, all released, then 2 new LPs allocated - Legacy reported 3 transponders (1 + 2) instead of 2. Also regenerates expected results for SNR recheck test fixtures to reflect the corrected transponder counting behavior. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Pass routing weight (e.g., XT-aware normalized cost) through the slicing pipeline to lightpaths instead of recalculating raw path length. This ensures weights_dict and mods_used_dict.*.length track the actual routing algorithm's weight, not just physical distance. - metrics.py: use lp.path_weight_km instead of _calculate_path_length_new() - slicing_pipeline.py: add path_weight parameter to try_slice and sub-methods - orchestrator.py: pass weight_km to slicing.try_slice() 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Add PathOption frozen dataclass for RL action selection representing candidate paths with metadata. This is Chunk 1 of Phase 4 RL integration. - PathOption with 13 fields (path_index, path, weight_km, num_hops, modulation, slots_needed, is_feasible, congestion, available_slots, spectrum_start, spectrum_end, core_index, band) - Validation in __post_init__ for invariants - compute_action_mask() helper for action masking - Type aliases: PathOptionList, ActionMask - 20 unit tests covering creation, immutability, validation, action mask Location: fusion/modules/rl/adapter/ (integrates with existing RL code) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Add RLSimulationAdapter class with __init__ and pipeline property accessors. This is Chunk 2 of Phase 4 RL integration. - RLSimulationAdapter stores orchestrator reference - Properties expose routing and spectrum pipelines - Critical invariant: adapter.routing IS orchestrator.routing (identity) - Validation raises ValueError for None orchestrator - 8 unit tests verifying pipeline identity and initialization The adapter ensures RL code uses the exact same pipeline instances as non-RL simulation, eliminating duplicated logic. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Add get_path_options() method to RLSimulationAdapter that queries routing and spectrum pipelines to get candidate paths with feasibility. This is Chunk 3 of Phase 4 RL integration. - Calls routing pipeline to get k candidate paths - Calls spectrum pipeline (read-only) to check feasibility per path - Creates PathOption for each path with: - Path geometry (nodes, weight_km, num_hops) - Modulation and slots_needed from spectrum result - is_feasible from real spectrum check - Placeholder congestion/available_slots (0.0/1.0) - Returns empty list if no routes found - Handles paths without modulation (marked infeasible) - 10 new unit tests covering all scenarios TODO: Implement _compute_path_congestion and _compute_available_slots when NetworkState provides get_link_utilization/get_available_slots. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Add apply_action() method to RLSimulationAdapter that routes RL actions through the orchestrator with forced_path. This is Chunk 4 of Phase 4. - Finds PathOption matching the action index - Calls orchestrator.handle_arrival with forced_path - Returns AllocationResult from orchestrator - Returns failed result if action doesn't match any PathOption - Raises ValueError for negative actions - 7 new unit tests verifying orchestrator integration Key invariant: All allocation logic (spectrum, SNR, grooming, slicing) goes through the same code path as non-RL simulation. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Add compute_reward() method to RLSimulationAdapter that computes reward signals from AllocationResult. This is Chunk 5 of Phase 4. Reward structure: - Success: +1.0 - Failure: -1.0 - Grooming bonus: +0.1 (used existing lightpath capacity) - Slicing penalty: -0.05 (request was split) - Uses sensible default values (configurable rewards deferred) - Safely handles missing is_groomed/is_sliced attributes - 7 new unit tests covering all reward scenarios This completes Phase 4.1 (RLSimulationAdapter) - Integration Checkpoint 1. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Complete Phase 4.1 RLSimulationAdapter scaffolding: - Add RLConfig dataclass for configurable RL settings - Add DisasterState dataclass for survivability scenarios - Add config parameter to RLSimulationAdapter.__init__ - Add get_action_mask() method to adapter - Add build_observation() method for online RL - Add build_offline_state() method for BC/IQL compatibility - Add OfflinePolicyAdapter class for offline policy integration - Add create_disaster_state_from_engine() factory function - Add from_pipeline_results() factory to PathOption - Add disaster-aware fields to PathOption (frag_indicator, failure_mask, dist_to_disaster, min_residual_slots) - Update compute_reward() to use config values - Implement _compute_path_congestion() and _compute_available_slots() - Add PHASE4_IMPLEMENTATION_PLAN.md for tracking 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Add Phase 4.2 Chunk 6 - UnifiedSimEnv skeleton with: - UnifiedSimEnv class in fusion/modules/rl/environments/ - observation_space: Dict with 8 Box spaces (source, destination, holding_time, slots_needed, path_lengths, congestion, available_slots, is_feasible) - action_space: Discrete(k_paths) - Stub reset() and step() methods (to be implemented in chunks 7-8) - action_mask in info dict for SB3 MaskablePPO compatibility - Comprehensive tests verifying Gymnasium space validity 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Add Phase 4.2 Chunk 7 - Full reset() implementation: - Add SimpleRequest dataclass for standalone testing - Implement request generation with seeded RNG - Generate path feasibility for action masking - Build observations from current request: - One-hot encoded source/destination - Normalized holding time - Path features (slots_needed, path_lengths, congestion, available_slots, is_feasible) - Info dict includes action_mask, request_index, total_requests - Properties for episode state access (current_request, request_index, num_requests, is_episode_done) - Deterministic seeding: same seed produces identical episodes - Comprehensive tests for reset, seeding, and observation building 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Add Phase 4.2 Chunk 8 - Full step() implementation: - Implement step() that processes action and advances episode - Compute reward based on path feasibility: - Positive reward (rl_success_reward) for feasible paths - Negative reward (rl_block_penalty) for infeasible/invalid actions - Advance request_index and update current_request after each step - Generate new path feasibility for next request - Terminate episode when all requests processed - Proper error handling: - RuntimeError if step() called before reset() - RuntimeError if step() called after episode termination - Add _compute_reward() and _record_step_result() helper methods - Comprehensive tests for step, termination, rewards, and full episodes 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Add Phase 4.2 Chunk 9 - Integration Checkpoint 2: - Add tests using gymnasium.utils.env_checker.check_env() - Environment passes all Gymnasium compliance checks - Verified with both default and custom configurations This marks the first real RL verification point - the environment is now fully Gymnasium-compliant and ready for RL training. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Add Phase 4.2 Chunk 10 - ActionMaskWrapper for SB3 compatibility: - Create wrappers.py with ActionMaskWrapper class - Wrapper exposes action_masks() method required by MaskablePPO - Extracts mask from info["action_mask"] and caches it - Passes through all other methods to wrapped environment - Update __init__.py to export ActionMaskWrapper - Comprehensive tests for wrapper functionality 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Add Phase 4.2 Chunk 11 - Integration Checkpoint 3: - Add integration tests with sb3-contrib MaskablePPO - Use "MultiInputPolicy" for Dict observation spaces - Tests verify: - Can create MaskablePPO model with wrapped environment - Can call predict() with action masks - Can train for 1000 timesteps without crashing - Can train and evaluate the model - Tests skipped if sb3-contrib not installed This marks the third integration checkpoint - the environment is now verified to work with real RL training. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Add Phase 4.2 Chunk 12 - GNN observation mode: - Add use_gnn_obs and num_node_features to RLConfig - When enabled, observation space includes: - adjacency: (num_nodes, num_nodes) symmetric adjacency matrix - node_features: (num_nodes, num_node_features) per-node features - Node features include: - utilization: link utilization around node - degree: normalized node degree - centrality: betweenness centrality approximation - is_src_dst: source/destination indicator (1.0/0.5/0.0) - Add _generate_adjacency() and _generate_node_features() methods - Update _zero_observation() for GNN mode - GNN mode passes gymnasium env_checker - Comprehensive tests for GNN observation shapes and values 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

- Add NetworkState.reset() for RL episode resets - Add SimulationEngine RL methods: get_next_request(), process_releases_until(), record_allocation_result(), reset_rl_state(), num_requests property - Wire UnifiedSimEnv to RLSimulationAdapter with wired/standalone modes - Add PyG-format graph observations: edge_index [2,E], edge_attr, path_masks - Add PathEncoder class for path-to-edge mask encoding - Add configurable observation space (obs_1 through obs_8) via RLConfig.obs_space - Add request_bandwidth one-hot feature to observations - Update tests for new observation keys and add tests for PyG/PathEncoder 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

- Fix iteration tracking in unified_env.py to use self.iteration instead of hardcoded 0, ensuring iter_stats are saved per-iteration correctly - Simplify compute_reward in rl_adapter.py to match legacy behavior (raw reward/penalty without slicing/grooming modifiers) - Sync reward/penalty values from engine_props in unified_env reset - Remove debug prints from metrics.py and other files - Clean up unused debug methods in sdn_controller.py - Add req_id parameter to handle_test_train_step for tracking 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

- Add migration compatibility, deprecation, and factory tests for gymnasium envs - Add P4.1 adapter integration tests - Update test fixtures for epsilon_greedy_bandit and usbackbone scenarios - Update run_comparison.py case filter Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Implement P5.1 of the ML Control + Protection Integration phase: ControlPolicy Protocol: - Add @runtime_checkable protocol with select_action(), update(), get_name() - Export from fusion.interfaces PathOption Extension: - Add protection fields: backup_path, backup_feasible, backup_weight_km, backup_modulation, is_protected - Add both_paths_feasible property for 1+1 protection scenarios - Add total_weight_km and backup_hop_count properties - Add factory methods: from_protected_route(), from_unprotected_route() - Add validation for protection field consistency RLPolicy Wrapper: - Wrap pre-trained SB3 models to implement ControlPolicy - Apply feasibility masking during prediction - Fallback to first feasible action if model predicts infeasible - Return -1 when no feasible actions available - Add from_file() classmethod for loading from stable_baselines3/sb3_contrib - update() is no-op (pre-trained models) Tests: - Protocol isinstance checks - PathOption protection fields and both_paths_feasible property - RLPolicy prediction, masking, fallback behavior, and model loading Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Add five heuristic path selection policies: - FirstFeasiblePolicy: Select first feasible path (K-shortest first fit) - ShortestFeasiblePolicy: Select shortest feasible path by distance - LeastCongestedPolicy: Select least congested feasible path - RandomFeasiblePolicy: Random selection among feasible paths (seedable) - LoadBalancedPolicy: Balance path length and congestion (configurable alpha) All policies implement ControlPolicy protocol with update() as no-op. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Add MLControlPolicy supporting multiple model formats: - PyTorch (.pt/.pth) via TorchModelWrapper - Sklearn (.joblib/.pkl) via SklearnModelWrapper - ONNX (.onnx) via OnnxModelWrapper Features: - FeatureBuilder for observation construction - Robust fallback to heuristic on inference failure - Action masking for feasibility constraints - Deterministic inference (no SB3 dependency) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Add 1+1 dedicated protection support: DisjointPathFinder: - Link-disjoint paths via NetworkX edge_disjoint_paths - Node-disjoint paths via iterative node removal - Verification methods for disjointness ProtectionPipeline: - allocate_protected() finds common spectrum on both paths - Same spectrum slots on primary and backup (1+1 dedicated) - Configurable switchover latency Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Add policy-based path selection to orchestrator: SDNOrchestrator: - Optional policy, rl_adapter, protection_pipeline fields - handle_arrival_with_policy() for policy-driven selection - Backward compatible: handle_arrival() unchanged PolicyFactory: - Create heuristic/ml/rl policies from PolicyConfig - from_dict() for config file integration Protection gated: only used when protection_enabled + request.protection_required Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

Schema changes: - [policy_settings]: policy_type, policy_name, model_path, etc. - [heuristic_settings]: alpha, seed - [protection_settings]: protection_enabled, disjointness_type CLI arguments: - --policy-type, --policy-name, --policy-model-path - --heuristic-alpha, --heuristic-seed - --protection-enabled, --disjointness-type Template: policy_protection_example.ini with load_balanced + 1+1 protection Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

ryanmccann1024 and others added 30 commits October 2, 2025 10:57

chore: ignore generated diagrams in reports/diagrams

de95a65

Merge pull request #126 from SDNNetSim/docs/coding-testing-standards

da28e69

feat: add modern development tools and enhance coding standards

Merge pull request #127 from SDNNetSim/refactor/module-cleanup

6b2fbd0

refactor(fusion): complete module cleanup and coding standards compliance

Merge pull request #128 from SDNNetSim/refactor/unit-tests

222c132

test: comprehensive unit test suite for FUSION project

Merge pull request #129 from SDNNetSim/refactor/visualization

2fc431d

refactor(visualization): migrate to plugin-based architecture with hexagonal design

Merge branch 'release/6.0.0' into refactor/docs

a31f56a

Merge pull request #130 from SDNNetSim/refactor/docs

0198f09

docs: add codebase visualization guide and documentation improvements

chore(survivability): merge failures module into phase2 branch

a8a62d3

ryanmccann1024 and others added 24 commits January 5, 2026 10:55

ryanmccann1024 requested a review from arashr88 January 12, 2026 16:01

ryanmccann1024 self-assigned this Jan 12, 2026

arashr88 approved these changes Jan 13, 2026

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ryanmccann1024 merged commit 6566e18 into develop Jan 13, 2026
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ryanmccann1024 mentioned this pull request Jan 19, 2026

release(v6.0.0): merge develop into release branch #154

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ryanmccann1024 deleted the feature/v5-phase5-p51-control-policy branch January 19, 2026 19:12

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feat: FUSION v5 Architecture - Domain Model, State Management, Orchestrator, RL Integration, and Control Policies#152

feat: FUSION v5 Architecture - Domain Model, State Management, Orchestrator, RL Integration, and Control Policies#152
ryanmccann1024 merged 590 commits intodevelopfrom
feature/v5-phase5-p51-control-policy

ryanmccann1024 commented Jan 12, 2026

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ryanmccann1024 commented Jan 12, 2026

Feature Pull Request

Implementation Details

Architecture Overview

Phase 1: Core Domain Model

Phase 2: State Management

Phase 3: SDNOrchestrator

Phase 4: RL Integration

Phase 5: Control Policies + Protection

Testing

Performance Impact

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Backward Compatibility

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