🚧 Work in progress! 🚧
This library is developed to help with network modeling and capacity analysis use-cases. The graph implementation in this library is a wrapper around MultiDiGraph of NetworkX. Our implementation makes edges explicitly addressable which is important in traffic engineering applications.
The lib provides the following main primitives:
Besides, it provides a number of path finding and capacity calculation functions that can be used independently.
NetGraph can be used in two ways:
Prerequisites:
- Docker installed on your machine.
Steps:
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Clone the repository:
git clone https://github.com/networmix/NetGraph
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Build the Docker image:
cd NetGraph ./run.sh build -
Start the container with JupyterLab server:
./run.sh start
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Open the JupyterLab URL in your browser:
http://127.0.0.1:8788/
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Jupyter will show the content of
notebooksdirectory and you can start using the provided notebooks or create your own.
Note: Docker will mount the content of NetGraph directory into the /root/env directory inside container, so any changes made to the code in the NetGraph directory will be reflected in the container and vice versa.
The ngraph package is installed in the container in editable mode, so you can make changes to the code and see the changes reflected immediately in JupyterLab.
To exit the JupyterLab server, press Ctrl+C in the terminal where the server is running. To stop the remaining Docker container, run:
./run.sh stopPrerequisites:
- Python 3.8 or higher installed on your machine.
Note: Don't forget to use a virtual environment (e.g., venv) to avoid conflicts with other Python packages. See Python Virtual Environments for more information.
Steps:
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Install the package using pip:
pip install ngraph
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Use the package in your Python code:
from ngraph.lib.graph import MultiDiGraph from ngraph.lib.max_flow import calc_max_flow # Create a graph g = MultiDiGraph() g.add_edge("A", "B", metric=1, capacity=1) g.add_edge("B", "C", metric=1, capacity=1) g.add_edge("A", "B", metric=1, capacity=2) g.add_edge("B", "C", metric=1, capacity=2) g.add_edge("A", "D", metric=2, capacity=3) g.add_edge("D", "C", metric=2, capacity=3) # Calculate MaxFlow between the source and destination nodes max_flow = calc_max_flow(g, "A", "C") print(max_flow)
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Calculate MaxFlow across all possible paths between the source and destination nodes
# Required imports from ngraph.lib.graph import MultiDiGraph from ngraph.lib.max_flow import calc_max_flow # Create a graph with parallel edges # Metric: # [1,1] [1,1] # ┌────────►B─────────┐ # │ │ # │ ▼ # A C # │ ▲ # │ [2] [2] │ # └────────►D─────────┘ # # Capacity: # [1,2] [1,2] # ┌────────►B─────────┐ # │ │ # │ ▼ # A C # │ ▲ # │ [3] [3] │ # └────────►D─────────┘ g = MultiDiGraph() g.add_edge("A", "B", metric=1, capacity=1) g.add_edge("B", "C", metric=1, capacity=1) g.add_edge("A", "B", metric=1, capacity=2) g.add_edge("B", "C", metric=1, capacity=2) g.add_edge("A", "D", metric=2, capacity=3) g.add_edge("D", "C", metric=2, capacity=3) # Calculate MaxFlow between the source and destination nodes max_flow = calc_max_flow(g, "A", "C") # We can verify that the result is as expected assert max_flow == 6.0
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Calculate MaxFlow leveraging only the shortest paths between the source and destination nodes
# Required imports from ngraph.lib.graph import MultiDiGraph from ngraph.lib.max_flow import calc_max_flow # Create a graph with parallel edges # Metric: # [1,1] [1,1] # ┌────────►B─────────┐ # │ │ # │ ▼ # A C # │ ▲ # │ [2] [2] │ # └────────►D─────────┘ # # Capacity: # [1,2] [1,2] # ┌────────►B─────────┐ # │ │ # │ ▼ # A C # │ ▲ # │ [3] [3] │ # └────────►D─────────┘ g = MultiDiGraph() g.add_edge("A", "B", metric=1, capacity=1) g.add_edge("B", "C", metric=1, capacity=1) g.add_edge("A", "B", metric=1, capacity=2) g.add_edge("B", "C", metric=1, capacity=2) g.add_edge("A", "D", metric=2, capacity=3) g.add_edge("D", "C", metric=2, capacity=3) # Calculate MaxFlow between the source and destination nodes # Flows will be placed only on the shortest paths max_flow = calc_max_flow(g, "A", "C", shortest_path=True) # We can verify that the result is as expected assert max_flow == 3.0
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Calculate MaxFlow balancing flows equally across the shortest paths between the source and destination nodes
# Required imports from ngraph.lib.graph import MultiDiGraph from ngraph.lib.max_flow import calc_max_flow from ngraph.lib.common import FlowPlacement # Create a graph with parallel edges # Metric: # [1,1] [1,1] # ┌────────►B─────────┐ # │ │ # │ ▼ # A C # │ ▲ # │ [2] [2] │ # └────────►D─────────┘ # # Capacity: # [1,2] [1,2] # ┌────────►B─────────┐ # │ │ # │ ▼ # A C # │ ▲ # │ [3] [3] │ # └────────►D─────────┘ g = MultiDiGraph() g.add_edge("A", "B", metric=1, capacity=1) g.add_edge("B", "C", metric=1, capacity=1) g.add_edge("A", "B", metric=1, capacity=2) g.add_edge("B", "C", metric=1, capacity=2) g.add_edge("A", "D", metric=2, capacity=3) g.add_edge("D", "C", metric=2, capacity=3) # Calculate MaxFlow between the source and destination nodes # Flows will be equally balanced across the shortest paths max_flow = calc_max_flow( g, "A", "C", shortest_path=True, flow_placement=FlowPlacement.EQUAL_BALANCED ) # We can verify that the result is as expected assert max_flow == 2.0
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Place traffic demands leveraging all possible paths in a graph
# Required imports from ngraph.lib.graph import MultiDiGraph from ngraph.lib.common import init_flow_graph from ngraph.lib.demand import FlowPolicyConfig, Demand, get_flow_policy from ngraph.lib.flow import FlowIndex # Create a graph # Metric: # [1] [1] # ┌──────►B◄──────┐ # │ │ # │ │ # │ │ # ▼ [1] ▼ # A◄─────────────►C # # Capacity: # [15] [15] # ┌──────►B◄──────┐ # │ │ # │ │ # │ │ # ▼ [5] ▼ # A◄─────────────►C g = MultiDiGraph() g.add_edge("A", "B", metric=1, capacity=15, label="1") g.add_edge("B", "A", metric=1, capacity=15, label="1") g.add_edge("B", "C", metric=1, capacity=15, label="2") g.add_edge("C", "B", metric=1, capacity=15, label="2") g.add_edge("A", "C", metric=1, capacity=5, label="3") g.add_edge("C", "A", metric=1, capacity=5, label="3") # Initialize a flow graph r = init_flow_graph(g) # Create traffic demands demands = [ Demand("A", "C", 20), Demand("C", "A", 20), ] # Place traffic demands onto the flow graph for demand in demands: # Create a flow policy with required parameters or # use one of the predefined policies from FlowPolicyConfig flow_policy = get_flow_policy(FlowPolicyConfig.TE_UCMP_UNLIM) # Place demand using the flow policy demand.place(r, flow_policy) # We can verify that all demands were placed as expected for demand in demands: assert demand.placed_demand == 20 assert r.get_edges() == { 0: ( "A", "B", 0, { "capacity": 15, "flow": 15.0, "flows": { FlowIndex(src_node="A", dst_node="C", flow_class=0, flow_id=1): 15.0 }, "label": "1", "metric": 1, }, ), 1: ( "B", "A", 1, { "capacity": 15, "flow": 15.0, "flows": { FlowIndex(src_node="C", dst_node="A", flow_class=0, flow_id=1): 15.0 }, "label": "1", "metric": 1, }, ), 2: ( "B", "C", 2, { "capacity": 15, "flow": 15.0, "flows": { FlowIndex(src_node="A", dst_node="C", flow_class=0, flow_id=1): 15.0 }, "label": "2", "metric": 1, }, ), 3: ( "C", "B", 3, { "capacity": 15, "flow": 15.0, "flows": { FlowIndex(src_node="C", dst_node="A", flow_class=0, flow_id=1): 15.0 }, "label": "2", "metric": 1, }, ), 4: ( "A", "C", 4, { "capacity": 5, "flow": 5.0, "flows": { FlowIndex(src_node="A", dst_node="C", flow_class=0, flow_id=0): 5.0 }, "label": "3", "metric": 1, }, ), 5: ( "C", "A", 5, { "capacity": 5, "flow": 5.0, "flows": { FlowIndex(src_node="C", dst_node="A", flow_class=0, flow_id=0): 5.0 }, "label": "3", "metric": 1, }, ), }