As Kubernetes adoption grows, businesses are looking for ways to best optimize and maximize their investment in this powerful container orchestration tool. One way they can save on costs is by leveraging InsightFinder’s AI driven self-healing and self-optimizing capabilities.
Comprehensive Platform to Analyze, Optimize, and Predict Kubernetes workload performance
InsightFinder is a comprehensive machine learning-powered platform that integrates with Kubernetes, enabling businesses to analyze, optimize, and predict the performance of their workloads. Current customers have seen time and cost savings by implementing InsightFinder along with their Kubernetes usage, eliminating almost all crash incidents and saving 60% on cloud costs. We’ll explain the benefits below:
InsightFinder KubeInsight offers comprehensive integration capabilities, enabling the collection of metrics, logs, and event data directly from pods and containers within a Kubernetes cluster. The platform allows businesses to analyze all machine data from different platforms including AWS EKS, Google Cloud GKE, and on-premise solutions, making it a versatile solution for multi-cloud strategies.
InsightFinder collects a wide range of data points to help businesses make informed decisions. The platform collects critical performance metrics like CPU usage, memory usage, disk space usage, requests, and limits. It also offers visibility into network I/O, disk I/O, and container status events, crash events, and scaling events.
One of the most significant benefits of using KubeInsight is that it allows for efficient resource management through proactive vertical and horizontal pod scaling in response to workload surges. The platform leverages the award-winning unsupervised machine learning technologies to predict resource usage and provide in-depth root cause analysis for all container incidents such as crashes or out of memory failures. Additionally, integration with InsightFinder’s predictive autoscaler feature allows for cost-efficient resource usage, resulting in lower infrastructure and operational expenses.
Another notable advantage of using InsightFinder is its predictive auto-scaling feature, which supports the scaling of memory and CPU resources based on its predictions. Although most organizations have adopted reactive horizontal pod scaling, most kubernetes clusters still suffer from low resource utilization and numerous failures. This technology is based on the research discussed in“CloudScale: Elastic Resource Scaling for Multi-Tenant Cloud Systems”, the 10-year Best Paper Award winner, co-authored by InsightFinder founder Dr. Helen Gu. This innovation predicts and detects resource demand changes in real-time, permitting tailored scaling for specific events. This functionality enables proactive reductions of memory allocation, resulting in aligned resource allocation with predicted needs, and ultimately reducing costs.
InsightFinder’s predictive capabilities extend beyond resource allocation, making it a powerful tool for businesses looking to improve reliability and reduce expenses. By leveraging its algorithms, businesses can detect and optimize their workloads, reducing the risk of operational issues and downtime.
Overall, InsightFinder allows businesses to save money and time by optimizing their Kubernetes implementation and reducing costs. By leveraging its AI powered prediction capabilities, businesses can make informed decisions, efficiently allocate resources, and reduce the risk of operational outages. As Kubernetes adoption continues to grow, businesses must invest in solutions like InsightFinder to remain competitive and maximize their investment. At InsightFinder, we remain committed to developing innovative solutions that help our customers streamline their operations and stay ahead of the curve.
Learn more about InsightFinder’s Kubernetes integration here, and sign up for a free trial to start saving time and money with your data!