Benchmarking AIOps-Driven Fault Prediction and Prevention in Cloud-Native Environments

Cloud-native environments are gaining popularity due to the scalability, flexibility and cost-efficiency they offer.Yet still there are many challenges for the efficient control of these complex and evolving systems, and notably for the support of dependability, downtime minimisation and an anticipation to faults.To address these challenges, we discuss possible approaches to auto-defect prediction and preventing in cloud-native with AI and AIOps.This paper has a comprehensive plan for the proactive fault prediction, detection, and mitigation of cloud-native infrastructures with the use of AI and AIOps.For cloud-native environments where applications and services are deployed throughout numerous microservices, containers, and virtual machines, the complexity and opportunities of failures in the system become more.Given the scale at which these deployments run and the speed at which they run, conventional methods of monitoring and troubleshooting are not good enough.To process the large amount of operational data and predict potential mistakes, that have an impact on the behaviour of a system, Artificial Intelligence (AI), in particular machine learning algorithms, can be employed.The potential faults in cloud-native can be foreseen by combining the real-time monitoring data with the AI-based model in the system.An Unmatched defence The platform uses anomaly detection, predictive analytics, and intelligent automation to spot irregularities and deviant behaviours anywhere within the environment.This enables it to avert failures by initiating prophylactic action.To ensure further uptime of the system, the AI-driven problem prediction is integrated with AIOps-based automation and will kick in and pro-actively take actions such as scaling resources, restarting services or re-routing traffic, automatically.Even in environments where cloud-native settings change and evolve, these learning based approaches can make failure predictions and prevention techniques applicable.AIOps integration reduces response times and system down times and makes sure answers are automatically triggered without human interference.The proposed AI and AIOps based approach are demonstrated to reduce fault rate, increase system reliability and operational efficiency through several real-world cloud native systems case study and test.It has covered a gap about 40% more compared to the traditional tactics; in terms of error detection, time-to-repair and time-torecurrence, showing a significant a remarkable enhancement on increasing performance of error detection, increased time just saving 5 hours, and higher time-to-recurrence, showing better numbers than the typical qualify of appealing content produced.The findings illustrate how AI and AIOps can have a game-changing impact on cloud-native IT operations, delivering a firm footing for proactive fault management and prevention.With more and more organisations turning to cloud-based systems, this AI solution will be a key factor in maintaining the reliability and performance of today's IT operations.