Autonomous Link Maintenance (ALM) Plugin

The primary objective of the Autonomous Link Maintenance (ALM) plugin is to enhance cluster availability and improve the rate of job completion. This objective is accomplished by utilizing machine learning (ML) models to predict potential link failures. The plugin then isolates the expected failing links, implements maintenance procedures on them, and subsequently restores the fixed links to their original state by removing the isolation.

The ALM plugin performs the following tasks:

1. Collects telemetry data from UFM and employs ML jobs to predict which ports need to be isolated/de-isolated

2. Identifies potential link failures and isolates them to avert any interruption to traffic flow

3. Maintains a record of maintenance procedures that can be executed to restore an isolated link

4. After performing the required maintenance, the system verifies if the links can be de-isolated and restored to operational status (brought back online)

The ALM plugin operates in the following two distinct modes:

1. Shadow mode

   • Collects telemetry data, runs ML prediction jobs, and saves the predictions to files.

2. Active mode

   • Collects telemetry data, runs ML prediction jobs, and saves the predictions to files.

   • Automatically isolates and de-isolates based on predictions.

   • It is essential to note that a subset of the links must be specified in the allow list to enable this functionality.

The Autonomous Link Maintenance (ALM) plugin can be deployed using the following methods:

1. On the UFM Appliance

2. On the UFM Software

To deploy the plugin, follow these steps:

1. Download the ufm-plugin-alm-image from the NVIDIA License Portal (NLP).

2. Load the downloaded image onto the UFM server. This can be done either by using the UFM GUI by navigating to the Settings -> Plugins Management tab or by loading the image via the following instructions:

3. Log in to the UFM server terminal.

4. Run:

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   docker load -I <path_to_image> 
           

5. After successfully loading the plugin image, the plugin should become visible within the plugins management table within the UFM GUI. To initiate the plugin’s execution, simply right-click on the respective in the table.

The ALM plugin collects data from the UFM Enterprise appliance in the following two methods:

1. Low-frequency collection: This process occurs every 7 minutes and gathers data for the following counter: hist0, hist1, hist2, hist3, hist4, phy_effective_errors, phy_symbol_errors

2. High-frequency collection : This process occurs every 10 seconds and gathers data for the following counters: phy_state,logical_state,link_speed_active,link_width_active,fec_mode_active, raw_ber,eff_ber,symbol_ber,phy_raw_errors_lane0,phy_raw_errors_lane1,phy_raw_errors_lane2, phy_raw_errors_lane3,phy_effective_errors,phy_symbol_errors,time_since_last_clear, hist0,hist1,hist2,hist3,hist4,switch_temperature,CableInfo.temperature,link_down_events, plr_rcv_codes,plr_rcv_code_err,plr_rcv_uncorrectable_code,plr_xmit_codes,plr_xmit_retry_codes, plr_xmit_retry_events,plr_sync_events,hi_retransmission_rate,fast_link_up_status, time_to_link_up,status_opcode,status_message,down_blame,local_reason_opcode, remote_reason_opcode,e2e_reason_opcode,num_of_ber_alarams,PortRcvRemotePhysicalErrorsExtended, PortRcvErrorsExtended,PortXmitDiscardsExtended,PortRcvSwitchRelayErrorsExtended,PortRcvConstraintErrorsExtended, VL15DroppedExtended,PortXmitWaitExtended,PortXmitDataExtended,PortRcvDataExtended,PortXmitPktsExtended, PortRcvPktsExtended,PortUniCastXmitPktsExtended,PortUniCastRcvPktsExtended,PortMultiCastXmitPktsExtended,PortMultiCastRcvPktsExtended

3. The collected counters can be configurable and customized to suit your requirements. The counters can be found at /opt/ufm/conf/plugins/alm/counters.cfg
   

   

The ALM configuration is used for controlling isolation/de-isolation. The configuration can be found under /opt/ufm/cyber-ai/conf/cyberai.cfg.

After the successful deployment of the plugin, a new item is shown in the UFM side menu for the ALM plugin: 

Current State

This page displays a table presenting the current cluster status, outlining the following counts:

1. Number of ports

2. Number of isolated ports

3. Number of ports in active/shadow prediction mode

4. Number of ports in active/shadow NOC mode

5. Number of ports out of NOC

Events Summary

This page displays a table presenting a port count summary, outlining the following counts:

1. Number of isolated ports in the past hour, week, and month for ‘host to switch’ and ‘switch to switch’.

2. Number of de-isolated ports in the past hour, week, and month for ‘host to switch’ and ‘switch to switch’.

3. Number of isolation actions not taken from prediction by ALM in the past hour, week, and month for ‘host to switch’ and ‘switch to switch’.

4. Number of isolation actions not taken from NOC by ALM in the past hour, week, and month for ‘host to switch’ and ‘switch to switch’.

Port Level Status

This page displays a table presenting the cluster ports.

Model Performance

Different metrics can measure the performance and accuracy of the ALM model, these metrics will be exposed to the user through the table as follows

Configuration

This page displays ALM plugin configuration update method.

The ALM configuration is divided into four sections:

• General Configurations

• Prediction Mode

• NOC Mode

• ML Model Configurations

The table presented below displays the names and descriptions of ALM jobs. These jobs are designed to predict the ports that require isolation/de-isolation. Upon enabling the ALM plugin, these ALM jobs run periodically.