Configuration Examples

This section provides complete, ready-to-use configuration examples for different simulation scenarios. Each example includes the full configuration file and explanation of key parameters.

Basic Simulation 

Standard K-Shortest Path Simulation 

The most common simulation type - routing requests using K-shortest paths with first-fit spectrum allocation.

Use Case: Baseline performance evaluation, comparison studies

Configuration:

; =============================================================
; Basic K-Shortest Path Simulation
; Purpose: Standard routing with first-fit allocation
; =============================================================

[general_settings]
; --- Traffic Parameters ---
erlang_start = 300          ; Starting traffic load
erlang_stop = 1200          ; Ending traffic load
erlang_step = 100           ; Increment between loads
num_requests = 1000         ; Requests per iteration
holding_time = 3600         ; Average holding time (seconds)

; --- Simulation Control ---
max_iters = 5               ; Statistical iterations per Erlang point

; --- Routing Configuration ---
network = NSFNet            ; Network topology
k_paths = 3                 ; Number of candidate paths
route_method = k_shortest_path
allocation_method = first_fit

; --- Output Control ---
print_step = 0              ; 0 = no progress printing
save_snapshots = False

[topology_settings]
cores_per_link = 1          ; Single-core fiber
bw_per_slot = 12.5          ; 12.5 GHz per slot

[spectrum_settings]
c_band = 320                ; 320 slots = 4 THz C-band

[file_settings]
file_type = json            ; Output format

Run Command:

python -m fusion.cli.run_sim run_sim \
    --config_path basic_simulation.ini \
    --run_id baseline_nsfnet

Key Parameters Explained:

Parameter	Explanation
erlang_start/stop/step	Defines the traffic load sweep range (300 to 1200 Erlangs)
max_iters	Number of independent trials for statistical confidence
k_paths	Number of candidate routes to compute (more = better blocking, higher computation)
route_method	Algorithm for computing paths (k_shortest_path is standard)
allocation_method	How spectrum slots are assigned (first_fit is simplest)

Multi-Core Fiber Simulation 

Simulation with space-division multiplexing (SDM) using multiple cores per fiber.

Use Case: Evaluating SDM networks, crosstalk studies

Configuration:

; =============================================================
; Multi-Core Fiber Simulation
; Purpose: SDM network with multiple cores per link
; =============================================================

[general_settings]
erlang_start = 1000         ; Higher load for SDM capacity
erlang_stop = 3000
erlang_step = 200
num_requests = 2000
holding_time = 3600
max_iters = 5
network = Pan-European
k_paths = 4
route_method = k_shortest_path
allocation_method = first_fit

[topology_settings]
; Multi-core configuration
cores_per_link = 7          ; 7-core fiber
bw_per_slot = 12.5

[spectrum_settings]
c_band = 320

[snr_settings]
; Crosstalk awareness
snr_type = None             ; No SNR calculation
xt_type = None              ; Crosstalk disabled for basic SDM

Run Command:

python -m fusion.cli.run_sim run_sim \
    --config_path multicore_simulation.ini \
    --run_id sdm_pan_european

SNR-Aware Simulation 

Physical Layer Impairment-Aware Routing 

Simulation that considers signal quality degradation along the optical path.

Use Case: Realistic network modeling, modulation format selection

Configuration:

; =============================================================
; SNR-Aware Simulation
; Purpose: Physical layer impairment-aware routing
; =============================================================

[general_settings]
erlang_start = 300
erlang_stop = 900
erlang_step = 100
num_requests = 1000
holding_time = 3600
max_iters = 5
network = NSFNet
k_paths = 5                 ; More paths for SNR-aware selection
route_method = k_shortest_path
allocation_method = first_fit

; SNR-specific routing options
mod_assumption = REACH_BASED    ; Modulation based on reach

[topology_settings]
cores_per_link = 1
bw_per_slot = 12.5

[spectrum_settings]
c_band = 320

[snr_settings]
; Enable SNR calculation
snr_type = snr_calculation
snr_check = True            ; Check SNR before allocation

; Physical parameters
input_power = 0.001         ; Launch power (W)
beta = 0.5                  ; Spontaneous emission factor
theta = 1.0                 ; Noise figure factor

; Modulation thresholds (dB)
; Each modulation format has minimum SNR requirement
; Format: [bits_per_symbol, min_snr_db, max_reach_km]

Run Command:

python -m fusion.cli.run_sim run_sim \
    --config_path snr_aware.ini \
    --run_id snr_study

SNR Parameters Explained:

Parameter	Explanation
snr_type	`snr_calculation` enables SNR computation, `None` disables
snr_check	If True, verify SNR meets modulation threshold before allocation
input_power	Optical launch power (impacts both signal and nonlinear noise)
beta	Amplifier spontaneous emission factor (affects ASE noise)
mod_assumption	`REACH_BASED` selects modulation by distance, `XTAR_ASSUMPTIONS` for crosstalk-aware

Crosstalk-Aware Simulation 

Inter-Core Crosstalk (XT) Modeling 

Simulation that models crosstalk between fiber cores in SDM networks.

Use Case: Multi-core fiber crosstalk studies, XT-aware resource allocation

Configuration:

; =============================================================
; Crosstalk-Aware Simulation (XTAR)
; Purpose: Inter-core crosstalk modeling in SDM networks
; =============================================================

[general_settings]
erlang_start = 2000         ; High load for XT impact
erlang_stop = 2100
erlang_step = 100
num_requests = 1500
holding_time = 3600
max_iters = 4
network = Pan-European
k_paths = 4
mod_assumption = XTAR_ASSUMPTIONS  ; Crosstalk-aware modulation

; XT-aware routing
route_method = xt_aware
allocation_method = priority_first
spectrum_priority = CSB     ; Core-Spectrum-Bandwidth priority

[topology_settings]
cores_per_link = 7          ; 7-core fiber (typical for XT studies)
bw_per_slot = 12.5

[spectrum_settings]
c_band = 320

[snr_settings]
; Enable crosstalk calculation
snr_type = xt_calculation
xt_type = with_length       ; Length-dependent crosstalk

; Crosstalk parameters
; coupling_coefficient determined by core spacing and fiber design
; Typical values: -30 to -40 dB/km for adjacent cores

[ml_settings]
; Optional: ML model for crosstalk prediction
ml_training = True
ml_model = decision_tree

Run Command:

python -m fusion.cli.run_sim run_sim \
    --config_path crosstalk_aware.ini \
    --run_id xtar_study

Crosstalk Parameters Explained:

Parameter	Explanation
xt_type	`with_length` models XT as function of distance, `without_length` uses fixed
route_method=xt_aware	Uses XT-aware routing algorithm
spectrum_priority	`CSB` = Core-Spectrum-Bandwidth, `SCB` = Spectrum-Core-Bandwidth
mod_assumption=XTAR_ASSUMPTIONS	Uses crosstalk-aware modulation format selection

Reinforcement Learning Configurations 

Online RL Training (Legacy)

Configuration for training RL agents using online simulation interaction.

Use Case: Training RL routing policies, algorithm development

Configuration:

; =============================================================
; Online RL Training Configuration (Legacy System)
; Purpose: Train RL agent through simulation interaction
; =============================================================

[general_settings]
erlang_start = 400
erlang_stop = 800
erlang_step = 100
num_requests = 500          ; Shorter episodes for faster training
holding_time = 3600
max_iters = 1               ; Single iteration per Erlang (RL handles exploration)
network = NSFNet
k_paths = 4

; RL routing mode
route_method = k_shortest_path

[topology_settings]
cores_per_link = 1
bw_per_slot = 12.5

[spectrum_settings]
c_band = 320

[rl_settings]
; --- Training Mode ---
is_training = True          ; Enable training mode
path_algorithm = dqn        ; DQN agent (options: dqn, ppo, a2c)

; --- Hyperparameters ---
learning_rate = 0.0003
gamma = 0.99                ; Discount factor
epsilon = 1.0               ; Initial exploration rate
epsilon_decay = 0.995       ; Exploration decay per episode
epsilon_min = 0.01          ; Minimum exploration

; --- Network Architecture ---
hidden_layers = [128, 64]   ; Neural network layers
batch_size = 32
memory_size = 10000         ; Replay buffer size

; --- Training Control ---
target_update_freq = 100    ; Steps between target network updates
save_freq = 1000            ; Steps between checkpoints

; --- Path Selection ---
path_levels = 4             ; Action space size (matches k_paths)

; --- Device ---
device = auto               ; auto/cpu/cuda

Run Command:

python -m fusion.cli.run_sim run_sim \
    --config_path rl_training.ini \
    --run_id rl_dqn_training

Offline RL with Behavior Cloning 

Configuration for training RL policies from pre-collected datasets (v6.0+).

Use Case: Learning from heuristic demonstrations, imitation learning

Configuration:

; =============================================================
; Offline RL Configuration (Behavior Cloning)
; Purpose: Train policy from offline dataset
; =============================================================

[general_settings]
erlang_start = 500
erlang_stop = 1000
erlang_step = 100
num_requests = 1000
holding_time = 3600
max_iters = 5
network = NSFNet
k_paths = 4

[topology_settings]
cores_per_link = 1
bw_per_slot = 12.5

[spectrum_settings]
c_band = 320

[policy_settings]
; Use offline RL policy
policy_type = bc             ; Behavior cloning
policy_name = bc_nsfnet_v1

[offline_rl_settings]
; Model paths
bc_model_path = models/bc/nsfnet_policy.pt
device = cpu

; Fallback when BC fails
fallback_policy = ksp_ff     ; Fall back to KSP + First-Fit

[dataset_logging_settings]
; Log dataset for future training
log_offline_dataset = True
dataset_output_path = data/offline_rl/bc_dataset.parquet
epsilon_mix = 0.1            ; 10% random exploration for diversity

Run Command:

python -m fusion.cli.run_sim run_sim \
    --config_path offline_rl_bc.ini \
    --run_id bc_evaluation

Offline RL with IQL 

Configuration for Implicit Q-Learning (IQL) policy evaluation.

Use Case: Conservative offline RL, avoiding out-of-distribution actions

Configuration:

; =============================================================
; Offline RL Configuration (IQL)
; Purpose: Conservative offline RL evaluation
; =============================================================

[general_settings]
erlang_start = 500
erlang_stop = 1000
erlang_step = 100
num_requests = 1000
holding_time = 3600
max_iters = 5
network = NSFNet
k_paths = 4

[topology_settings]
cores_per_link = 1
bw_per_slot = 12.5

[spectrum_settings]
c_band = 320

[policy_settings]
; Use IQL policy
policy_type = iql
policy_name = iql_nsfnet_conservative

[offline_rl_settings]
iql_model_path = models/iql/nsfnet_policy.pt
device = cpu
fallback_policy = ksp_ff

Run Command:

python -m fusion.cli.run_sim run_sim \
    --config_path offline_rl_iql.ini \
    --run_id iql_evaluation

Supervised Learning Configuration 

ML Model Training Mode 

Configuration for training supervised ML models (decision trees, etc.) for routing decisions.

Use Case: Feature analysis, interpretable routing models

Configuration:

; =============================================================
; Supervised Learning Configuration
; Purpose: Train ML model for routing decisions
; =============================================================

[general_settings]
erlang_start = 500
erlang_stop = 1000
erlang_step = 100
num_requests = 2000         ; More data for ML training
holding_time = 3600
max_iters = 3
network = NSFNet
k_paths = 4
route_method = k_shortest_path
allocation_method = first_fit

[topology_settings]
cores_per_link = 1
bw_per_slot = 12.5

[spectrum_settings]
c_band = 320

[ml_settings]
; Enable ML training
ml_training = True
ml_model = decision_tree    ; Options: decision_tree, random_forest, svm

; Model parameters
max_depth = 10
min_samples_split = 5

; Train/test split
test_size = 0.2             ; 20% for testing

; Model output
deploy_model = False        ; True to use trained model for inference

Run Command:

python -m fusion.cli.run_sim run_sim \
    --config_path ml_training.ini \
    --run_id ml_dt_training

ML Model Inference Mode 

Configuration for using a trained ML model for routing decisions.

Configuration:

; =============================================================
; ML Inference Configuration
; Purpose: Use trained ML model for routing
; =============================================================

[general_settings]
erlang_start = 500
erlang_stop = 1500
erlang_step = 100
num_requests = 1000
holding_time = 3600
max_iters = 5
network = NSFNet
k_paths = 4
route_method = k_shortest_path
allocation_method = first_fit

[topology_settings]
cores_per_link = 1
bw_per_slot = 12.5

[spectrum_settings]
c_band = 320

[ml_settings]
ml_training = False         ; Inference mode
deploy_model = True         ; Use trained model
model_path = models/ml/decision_tree_nsfnet.pkl

Run Command:

python -m fusion.cli.run_sim run_sim \
    --config_path ml_inference.ini \
    --run_id ml_evaluation

Survivability Configurations 

1+1 Protection (No Failures)

Configuration for dedicated path protection without failure injection.

Use Case: Baseline protection overhead measurement

Configuration:

; =============================================================
; 1+1 Protection Configuration
; Purpose: Dedicated path protection baseline
; =============================================================

[general_settings]
erlang_start = 200          ; Lower load due to protection overhead
erlang_stop = 600
erlang_step = 50
num_requests = 1000
holding_time = 3600
max_iters = 5
network = NSFNet
k_paths = 4

; Protection routing
route_method = 1plus1_protection

[topology_settings]
cores_per_link = 1
bw_per_slot = 12.5

[spectrum_settings]
c_band = 320

[protection_settings]
; 1+1 dedicated protection
protection_switchover_ms = 50    ; Switchover time in milliseconds
node_disjoint_protection = True  ; Node-disjoint backup paths

Run Command:

python -m fusion.cli.run_sim run_sim \
    --config_path protection_1plus1.ini \
    --run_id protection_baseline

Link Failure Experiment 

Configuration for simulating single link failures.

Use Case: Network resilience evaluation, protection effectiveness

Configuration:

; =============================================================
; Link Failure Experiment
; Purpose: Evaluate network resilience to link failures
; =============================================================

[general_settings]
erlang_start = 300
erlang_stop = 800
erlang_step = 100
num_requests = 2000         ; More requests to capture failure event
holding_time = 3600
max_iters = 10              ; More iterations for failure statistics
network = NSFNet
k_paths = 4
route_method = 1plus1_protection

[topology_settings]
cores_per_link = 1
bw_per_slot = 12.5

[spectrum_settings]
c_band = 320

[failure_settings]
; Single link failure
failure_type = link

; When failure occurs (request index)
t_fail_arrival_index = 500

; How long failure lasts (in arrivals)
t_repair_after_arrivals = 1000

; Which link fails (optional, random if not specified)
; failed_link = ["node_A", "node_B"]

[protection_settings]
protection_switchover_ms = 50
restoration_latency_ms = 100
revert_to_primary = True    ; Revert to primary after repair

[reporting_settings]
export_csv = True
csv_output_path = results/link_failure/

Run Command:

python -m fusion.cli.run_sim run_sim \
    --config_path link_failure.ini \
    --run_id link_fail_experiment

Node Failure Experiment 

Configuration for simulating node failures (more severe than link failures).

Configuration:

; =============================================================
; Node Failure Experiment
; Purpose: Evaluate network resilience to node failures
; =============================================================

[general_settings]
erlang_start = 200
erlang_stop = 600
erlang_step = 50
num_requests = 2000
holding_time = 3600
max_iters = 10
network = Pan-European      ; Larger network for node failure
k_paths = 5                 ; More paths for redundancy
route_method = 1plus1_protection

[topology_settings]
cores_per_link = 1
bw_per_slot = 12.5

[spectrum_settings]
c_band = 320

[failure_settings]
failure_type = node
t_fail_arrival_index = 500
t_repair_after_arrivals = 1500

; Node to fail (optional)
; failed_node = "node_5"

[protection_settings]
protection_switchover_ms = 50
node_disjoint_protection = True  ; Essential for node failure survival

Run Command:

python -m fusion.cli.run_sim run_sim \
    --config_path node_failure.ini \
    --run_id node_fail_experiment

SRLG Failure Experiment 

Configuration for Shared Risk Link Group failures (multiple simultaneous failures).

Configuration:

; =============================================================
; SRLG Failure Experiment
; Purpose: Evaluate resilience to correlated failures
; =============================================================

[general_settings]
erlang_start = 200
erlang_stop = 500
erlang_step = 50
num_requests = 2000
holding_time = 3600
max_iters = 10
network = USbackbone60      ; Large network with SRLG definitions
k_paths = 5
route_method = 1plus1_protection

[topology_settings]
cores_per_link = 1
bw_per_slot = 12.5

[spectrum_settings]
c_band = 320

[failure_settings]
failure_type = srlg         ; Shared Risk Link Group
t_fail_arrival_index = 500
t_repair_after_arrivals = 2000

; SRLG to fail (defined in network topology)
; srlg_id = "conduit_1"

[protection_settings]
protection_switchover_ms = 50
srlg_disjoint_protection = True  ; SRLG-disjoint backup paths

Run Command:

python -m fusion.cli.run_sim run_sim \
    --config_path srlg_failure.ini \
    --run_id srlg_fail_experiment

CI/CD and Testing Configurations 

Cross-Platform CI Configuration 

Configuration optimized for continuous integration pipelines.

Use Case: Automated testing, GitHub Actions, Jenkins

Configuration:

; =============================================================
; Cross-Platform CI Configuration
; Purpose: Fast, portable configuration for CI/CD
; =============================================================

[general_settings]
; Minimal load for fast execution
erlang_start = 300
erlang_stop = 400
erlang_step = 100
num_requests = 100          ; Minimal for quick tests
holding_time = 3600
max_iters = 1               ; Single iteration
network = NSFNet            ; Smallest standard topology
k_paths = 2                 ; Minimal paths

route_method = k_shortest_path
allocation_method = first_fit

; No output files to avoid path issues
print_step = 0
save_snapshots = False

[topology_settings]
cores_per_link = 1
bw_per_slot = 12.5

[spectrum_settings]
c_band = 320

[file_settings]
file_type = json

Run Command:

python -m fusion.cli.run_sim run_sim \
    --config_path fusion/configs/templates/cross_platform.ini \
    --run_id ci_test

Quick Validation Test 

Ultra-minimal configuration for syntax and import validation.

Configuration:

; =============================================================
; Quick Validation Configuration
; Purpose: Fastest possible validation run
; =============================================================

[general_settings]
erlang_start = 300
erlang_stop = 300           ; Single point
erlang_step = 100
num_requests = 10           ; Absolute minimum
holding_time = 3600
max_iters = 1
network = NSFNet
k_paths = 1                 ; Single path

[topology_settings]
cores_per_link = 1
bw_per_slot = 12.5

[spectrum_settings]
c_band = 320

Run Command:

python -m fusion.cli.run_sim run_sim \
    --config_path quick_test.ini \
    --run_id validate

Using Profiles Instead of Files 

For quick testing without creating files:

from fusion.configs import ConfigRegistry

registry = ConfigRegistry()

# Create quick test configuration programmatically
config_manager = registry.create_profile_config('quick_test')

# Or combine template with custom overrides
config_manager = registry.create_custom_config(
    'minimal',
    overrides={
        'general_settings.num_requests': 10,
        'general_settings.max_iters': 1,
    }
)

Configuration Comparison Summary 

Configuration Quick Reference
Use Case	Template	key_paths	max_iters	Key Parameters
Quick Test	minimal.ini	2-3	1-2	num_requests=100
Production	default.ini	3-4	5-10	num_requests=1000+
SNR Study	(custom)	4-5	5	snr_type=snr_calculation
Crosstalk	xtar_example	4	4	xt_type=with_length, cores=7
RL Training	(custom)	4	1	is_training=True
Offline RL	(custom)	4	5	policy_type=bc/iql
Protection	(custom)	4-5	5-10	route_method=1plus1_protection
Failure Test	(custom)	5	10	failure_type=link/node/srlg
CI/CD	cross_platform	2	1	num_requests=100