Lack of Disaster Recovery Plan for Critical Applications: Enhancements using ML Models to Achieve Zero Downtime and 100% Resource Backup
Author(s):
Praveen Kumar Thopalle
In the fast-paced digital world, ensuring the continuity and resilience of critical applications in the IT and software industries has become increasingly vital. Downtime, data loss, and service interruptions can have catastrophic effects, causing substantial financial losses, reputational damage, and loss of customer trust. Traditional disaster recovery (DR) approaches often fail to meet the demands of modern infrastructures due to their reliance on manual processes and reactive strategies. However, recent advancements in artificial intelligence (AI), particularly machine learning (ML), have opened new avenues for revolutionizing DR plans. This paper provides a comprehensive analysis of how ML models can enhance disaster recovery in IT environments by enabling real-time anomaly detection, predictive failure analysis, and automated failover processes. By integrating AI-driven techniques, businesses can aim for zero downtime and 100% resource backup, ensuring a more robust, scalable, and efficient recovery framework. The study examines how ML can optimize resource allocation, improve operational continuity, and address critical IT challenges, offering future-proof solutions to safeguard essential software and IT services.
