International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 02 | Feb 2024
p-ISSN: 2395-0072
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AI AND ITS IMPACT ON FULL STACK OBSERVABILITY Manoj Meenakshi Babu Dattatreya Cisco USA ------------------------------------------------------------------------***------------------------------------------------------------------ABSTRACT Artificial intelligence (AI) techniques such as machine learning and deep learning are transforming IT operations while enhancing entire stack observability. AI delivers additional capabilities such as automated anomaly detection, predictive analytics, intelligent alerting, and more to improve monitoring. The article looks at how AI is improving important features of full-stack observability. Keywords: Artificial intelligence (AI), Machine learning, Deep learning, IT operations, Full-stack observability
I.
INTRODUCTION
Observability is the ability to measure and understand a system's internal state using exterior outputs. Robust observability is essential for managing the complexity of today's IT settings. As systems grow in size and complexity, manual approaches fail. Teams become overloaded by billions of monitoring data points spread across thousands of servers and struggle to keep up. Despite enormous investments in monitoring techniques, outages can persist hours or even days. The high volume of notifications generated by legacy threshold-based alerting systems causes alert fatigue. Traditional methods for identifying root causes across interconnected microservices are time-consuming and slow.
Figure 1: A picture depicting the Impact of AI Artificial intelligence (AI) is the solution to these challenges. Modern AI capabilities such as machine learning, neural networks, and deep learning are ideal for IT operations (AIOps) application cases. AI improves complete stack observability by automatically learning patterns in big datasets, detecting abnormalities, and codifying tricky troubleshooting methods. Instead of simply collecting, displaying, and alerting on time series metrics, current
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