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The role of AI in Kubernetes monitoring



In a dynamic environment like Kubernetes, where manual tracking is impossible, AI-powered monitoring tools, such as Site24x7, surf through enormous amounts of data, detecting irregularities, predicting vulnerabilities, and alerting the user about a possible outage that is about to happen if the resource is not handled.

Proactive identification of abnormalities 

The AI-powered tool scans for subtle patterns in the existing model and proactively alerts about a possible performance bottleneck. It also detects and signals the user about anomalies from the usual pattern or workload behavior and ranks it based on its potential severity. 

Autoscaling issues in an e-commerce company 

An e-commerce company, Zylker, is using a Kubernetes setup with autoscaling capabilities to manage inconsistent web traffic during promotional sales. When the company's website experiences unexpected slowdowns during a sudden traffic surge, the IT teams struggle to pinpoint the issue, and they suspect misconfigurations. But it takes almost two days to pinpoint the exact problem.

If they had an AI-powered tool, this tool would proactively identify that certain pods are in the verge of consuming unusually high memory, deviating from the normal pattern during traffic spikes which might lead to bottlenecks in deployment. When the IT team is alerted, it can optimize resource allocation for the affected pods and ensure the website runs smoothly, saving time and revenue during important sales events.

Predictive scaling for optimal Kubernetes agility

Consider an autoscaled environment. The resource usage and consumption are often unnoticed until there is a bottleneck or outage. An AI-driven monitoring tool will study the usage flow for multiple critical indicators, including the CPU, memory, disk, network, and more, and predict future usage, enabling the user to optimize resource allocation. The tool also helps plan the capacity of the Kubernetes setup and manages it more efficiently.

Resource consumption challenges in financial organizations 

In the financial sector, high volumes of data are processed at the end of each quarter as the reports are generated in massive numbers. The increase in customer count has an impact on the number of transactions made in the application, making it hard to process a huge amount of data. During these times, the resource consumption can be flooded, especially the CPU, memory, and disk I/O on nodes that are responsible for processing the financial data. 


With an AI-powered monitoring tool, the system can predict these rising resource needs before they hit critical levels. By forecasting the future load and automatically adjusting resources through predictive scaling, the organization can ensure that processing remains smooth, avoids bottlenecks, and minimizes downtime during critical financial reporting periods.

If the CPU, memory, and disk I/O of the containers in the ZylkerNode1 node are witnessing a steady rise, the AI tool will predict the future usage flow of the container, indicating that it is nearing a critical state. The overutilization might hamper application deployment, which should happen effortlessly in an autoscaled environment. With this forecasted scaling, bottlenecks can be avoided to a greater extent, and downtimes can be minimized.

Best practices to sustain a fully functional cluster

AI predicts issues. AI provides best practice recommendations for your Kubernetes setup to ensure that it is up and running 24/7. To guide the user, the AI-powered tool inspects and analyzes the user's cluster and runs through its database to check if the setup is up to the ideal industry standards, that is, secure, optimal in performance, available, reliable, and easy to manage. Configurations that hinder proper functioning and the security of your workloads are spotted. AI detects loopholes and abnormalities, and generates reports to optimize your workloads to maintain peak performance and stay safe from security breaches.

Container security vulnerabilities in an IT organization 

An IT organization that is highly dependent on its applications will need to ensure that there are no loopholes in their Kubernetes infrastructure that gives way for unauthorized intrusions. For example, if they have deployed most of their containers with the Run-as-root privilege, then their Kubernetes setup is highly prone to unapproved intrusions.

When they employ an AI tool such as Site24x7, it will start to inspect the entire cluster, identifying the compromised containers, and recommends them to change the privilege for these containers to safeguard their cluster. It is highly recommended to update the privilege to a ReadOnlyFileSytem, so that they can shield their Kubernetes infrastructure from exposure and thwart an impending peril.

Automating root cause analysis for Kubernetes

The issues in a Kubernetes environment range from common configuration errors to inefficient resource allocation. At times, when encountering a problem or failure, the reason for the issue cannot be spotted within the stipulated time. It requires considerable manual labor and time. If the cause is not identified, the credibility and reliability of the organization as a whole will be at risk. When you employ AI, its root cause analysis reports will reduce the mean time to detect to the minimum, so IT teams can fix the issue before their customers are impacted. 

Application slowness setbacks in a governmental organization

Imagine a situation where a governmental organization, which handles millions of user's accounts, recognizes that a critical application hosted in a Kubernetes cluster is sluggish. During peak hours, the CPU usage and response rates experience spikes, which results in customer frustration. The IT team suspects that resource utilization is the issue and attempts to resolve issues manually. But this is in vain.


Fortunately, the IT team deploys an AI-powered Kubernetes monitoring tool, which more efficiently identifies that the slowness is due to the large number of queries executed during peak hours and identifies that some are unnecessary duplicates caused by a misconfigured service. As the tool detected the root cause, the IT team can work swiftly to optimize the query patterns and adjust the service configuration. 

What does the Site24x7 AI-powered Kubernetes monitoring tool provide? 

Here are five key benefits realized from deploying this solution: 



Employing an AI-powered tool, like the Site24x7 Kubernetes observability and monitoring tool, minimizes manual efforts and helps organizations retain their customer trust and loyalty by providing a seamless user experience.