Ai Driven MPLS Traffic optimization using real time composite scoring in GNS3

Published Aug 21, 2026
 8 hours to build
 Intermediate

This is a network optimization project that uses AI-based decision making to select the best path for MPLS traffic. Real time parameters such as latency, bandwidth, and packet loss are combined into a composite score. The system analyzes these parameters and dynamically identifies the most efficient path, improving network performance, reliability, and resource utilization a simulated GNS3 environment.

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Components Used

Router 7200
Used to create CE, PE and P routers in the MPLS network.
X 1
PC
Used as end devices to generate and transmit network traffic.
X 1
MPLS
Used for efficient packet forwarding through label switching.
X 1
OSPF
Used for routing and finding paths between routers.
X 1
Description

Step 1: Designed the Network Topology
The MPLS network topology was created in GNS3 using Cisco routers. The network consists of Customer Edge (CE), Provider Edge (PE), and Provider (P) routers. GNS3 allows the topology and router configurations to be tested in a simulated environment.

Step 2: Configured IP Addressing
IP addresses were assigned to the router interfaces and loopback interfaces. Connectivity between the routers was verified using the ping command.

Step 3: Configured OSPF Routing
OSPF was configured as the Interior Gateway Protocol to provide connectivity across the MPLS core. OSPF neighbors and routing tables were verified before moving to the next stage.

Step 4: Enabled MPLS and LDP
MPLS forwarding was enabled on the provider routers, and Label Distribution Protocol (LDP) was configured to distribute labels through the MPLS core.

Step 5: Configured PE and CE Networks
The CE routers were connected to the PE routers. The required routing and MPLS VPN configurations were applied to provide communication between customer networks through the MPLS core. A typical MPLS L3VPN setup uses OSPF, LDP, VRFs, and BGP VPNv4.

Step 6: Collected Network Performance Parameters
Network parameters such as latency, available bandwidth, and packet loss were considered to evaluate the condition of different paths.

Step7: AI-Based Path Selection
The AI-based decision mechanism analyzes the composite scores and identifies the most suitable path for traffic. This helps select a path based on current network conditions instead of relying only on a fixed routing decision.

Step 8: Tested the Network
Different traffic conditions were tested in GNS3. Connectivity, routing, latency, packet loss, and path selection were monitored to evaluate the proposed optimization method.

Codes

Downloads

MPLS project Download

Institute / Organization

Sinhgad College Of Engineering ,pune Savitribaifule pune university
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