Title :- Bandwidth Management System and Fault Prediction of MPLS Routers Deployed ON WAN using AI
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Implementation Plan:
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Scenario-1: LSTM Algorithm
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Step 1: Initially construct a network consisting of 15-IP MPLS routers and 45-access devices (Computers).

Step 2: Next we Implement CSPF routing algorithms in the MPLS network and Implement QoS in the network for various Class of Service.

Step 3: Then, we Generate Data on the network (Voice, video and Data) in the form of network packets.

Step 4: Next, we collect the effective bandwidth for each interface of the router.

Step 5: Then, we Create the dataset and we pre-process the dataset.

Step 6: We train and optimize the model using LSTM for predicting the future bandwidth.

Step 7: Integrate a SDN along with open Vswitch for prediction of bandwidth and optimise bandwidth.

Step 8: Performance metrics for the following

8.1: Performance of LSTM model – MAE, RMSE
8.2: Accuracy for LSTM model (Prediction vs Actual).
8.3: Time(s) vs. bandwidth (Kbits/sec)
8.4: Maximum rate(Mbps) vs. Detection Time(s)
8.5: Flow bandwidth(Mbps) vs. Flows

Scenario-2: ARIMA Algorithm
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Step 1: Initially construct a network consisting of 15-IP MPLS routers and 45-access devices (Computers).

Step 2: Next we Implement CSPF routing algorithms in the MPLS network and Implement QoS in the network for various Class of Service.

Step 3: Then, we Generate Data on the network (Voice, video and Data) in the form of network packets.

Step 4: Next, we collect the effective bandwidth for each interface of the router.

Step 5: Then, we Create the dataset and we pre-process the dataset.

Step 6: We train and optimize the model using ARIMA for predicting the future bandwidth.

Step 7: Integrate a SDN along with open Vswitch for prediction of bandwidth and optimise bandwidth.

Step 8: Performance metrics for the following

8.1: Performance of ARIMA model – MAE, RMSE
8.2: Accuracy for ARIMA model (Prediction vs Actual).
8.3: Time(s) vs. bandwidth (Kbits/sec)
8.4: Maximum rate(Mbps) vs. Detection Time(s)
8.5: Flow bandwidth(Mbps) vs. Flows

Scenario-3: MLP Algorithm
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Step 1: Initially construct a network consisting of 15-IP MPLS routers and 45-access devices (Computers).

Step 2: Next we Implement CSPF routing algorithms in the MPLS network and Implement QoS in the network for various Class of Service.

Step 3: Then, we Generate Data on the network (Voice, video and Data) in the form of network packets.

Step 4: Next, we collect the effective bandwidth for each interface of the router.

Step 5: Then, we Create the dataset and we pre-process the dataset.

Step 6: We train and optimize the model using MLP for predicting the future bandwidth.

Step 7: Integrate a SDN along with open Vswitch for prediction of bandwidth and optimise bandwidth.

Step 8: Performance metrics for the following

8.1: Performance of MLP model – MAE, RMSE
8.2: Accuracy for MLP model (Prediction vs Actual).
8.3: Time(s) vs. bandwidth (Kbits/sec)
8.4: Maximum rate(Mbps) vs. Detection Time(s)
8.5: Flow bandwidth(Mbps) vs. Flows

Software Requirement:
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1. Development Tool: Ns-3.35
2. Development OS: Ubuntu 22.04 LTS

Note:
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1) If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.

2) Please note that this implementation plan does not include any further steps after it is put into implementation.

3) This project is only based on simulations. Not a real time project.
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#MPLSFaultPrediction
#PerformanceAnalysis
#NetworkFaults
#MPLSPerformance
#PredictiveAnalysis
#FaultDetection
#NetworkPerformance
#MPLSAnalysis
#FaultPrediction
#PerformanceMetrics
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