Method and system for predicting and mitigating failures in VDI system

A computing system for predicting and mitigating failures in Virtual Desktop Infrastructure (VDI) systems, the computing system comprising:

• one or more processors;
• and
• a memory, wherein the memory stores processor-executable instructions, which, on execution, cause the one or more processors to:
• receive a plurality of system logs from a plurality of VDI systems;
• segregate one or more error logs from the plurality of system logs;
• generate a prediction score for each of the plurality of VDI systems based on respective one or more error logs, using a machine learning model, wherein the prediction score of a VDI system among the plurality of VDI systems is indicative of a possible failure in the VDI system, wherein the machine learning model is trained by performing steps of:
• receiving a plurality of feature vectors associated with a plurality of training error logs and one or more rules;
• and
• determining a failure and a value associated with the plurality of training error logs in the plurality of VDI systems, based on the plurality of feature vectors and the one or more rules;
• determining a correlation between the one or more rules, the determined failure and the plurality of feature vectors to train the machine learning model;
• wherein the trained machine learning model is used to predict failures in the plurality of VDI systems in real-time;
• predict a failure in at least one VDI system from the plurality of VDI systems based on the prediction score of the at least one VDI system and the respective one or more error logs using the trained machine learning model;
• and
• determine at least one response action associated with the predicted failure, thereby mitigating the failure in the plurality of VDI systems.

A non-transitory computer readable medium including instructions stored thereon that when processed by one or more processors, wherein the instructions cause a computing system to:

• receive a plurality of system logs from a plurality of VDI systems;
• segregate one or more error logs from the plurality of system logs;
• generate a prediction score for each of the plurality of VDI systems based on respective one or more error logs, using a machine learning model, wherein the prediction score of a VDI system among the plurality of VDI systems is indicative of a possible failure in the VDI system, wherein the machine learning model is trained by performing steps of:
• receiving a plurality of feature vectors associated with a plurality of training error logs and one or more rules;
• and
• determining a failure and a value associated with the plurality of training error logs in the plurality of VDI systems, based on the plurality of feature vectors and the one or more rules;
• determining a correlation between the one or more rules, the determined failure and the plurality of feature vectors to train the machine learning model;
• wherein the trained machine learning model is used to predict failures in the plurality of VDI systems in real-time;
• predict a failure in at least one VDI system from the plurality of VDI systems based on the prediction score of the at least one VDI system and the respective one or more error logs using the trained machine learning model;
• and
• determine at least one response action associated with the predicted failure, thereby mitigating the failure in the plurality of VDI systems.