Leverage Davis AI to Analyze your System before Things Break
Science & Technology
Introduction
Welcome to the observability clinic! We appreciate your participation in this live presentation. Today, we are excited to share a groundbreaking feature within Dynatrace's Davis AI. Many of you who have ventured into performance engineering will find this new capability invaluable and perhaps even fabulous.
Introduction
Today, we have Wolfgang Beer, a principal product manager, speaking about the newest improvements in Davis AI. We are looking at how Davis can now assist in exploratory analyses to optimize various systems, particularly Kubernetes workloads.
Housekeeping Rules
For our live audience, we encourage you to utilize the Q&A panel, as you won't be able to respond via microphone or chat directly. We will address your questions during the next hour.
The New Davis Exploratory AI Analysis Feature
Wolfgang expressed his excitement about finally pushing this new functionality into production. The feature allows for manual triggering of Davis AI analyses, marking a significant enhancement above the existing automation. The focus today is on how you can leverage this exploratory analysis capability effectively.
Understanding Davis AI Exploratory Analysis
Davis AI has been a reliable solution for detecting anomalies, reducing noise, and structuring problems over the past 18 months. The newly introduced feature—exploratory analysis—adds a layer on top of previous functionalities. It allows users to conduct deeper, on-demand investigations into data situations that can lead to system optimizations.
Manual Triggering
The exploratory analysis means that when you notice a peculiar trend in a chart, you can engage Davis AI with a simple click to analyze that metric and related signals automatically. This shifts from a reactive problem-solving approach to a proactive exploratory analysis.
Practical Application in Kubernetes
In our session, we delved into a Kubernetes use case. As organizations transition to Kubernetes, it is common to spin up multiple clusters efficiently. However, costs can spiral out of control quickly due to various factors such as cloud management fees, networking charges, and resource utilization rates.
Optimizing Costs
One key focus for organizations is optimizing charges based on workload resource definitions. The discussion covered critical concepts such as CPU requests and limits and their impacts on operational efficiency. In a practical example, an e-commerce company faced challenges related to excessive resource requests leading to throttling and service disruptions.
Leveraging Davis for Optimization
Through the use of Davis AI, performance engineers can interactively explore and analyze metrics to find inefficiencies within their workloads. With Davis's focus on correlation analysis, engineers can determine actionable insights that lead to informed adjustments of their workloads—saving time and improving efficiency.
Conclusion
In summary, this newly integrated exploratory analysis capability of Davis AI represents a transformative step for performance engineering. It empowers users to harness data intelligently, making it easier to optimize workloads and reduce operational costs effectively.
Documentation and Future Releases
All the details regarding the underlying algorithms, methodologies, and further enhancements to Davis AI will be documented transparently. Upcoming features include predictive analytics and event log signal integration to further enhance the capability of the platform.
Keyword
- Davis AI
- Exploratory Analysis
- System Optimization
- Kubernetes
- Performance Engineering
- Resource Requests
- Cost Optimization
- Throttling
FAQ
What is the new Davis AI exploratory analysis feature? The exploratory analysis feature allows users to manually trigger analyses on specific metrics to find correlations and optimize system performance.
How can I leverage Davis AI in a Kubernetes environment? Users can apply Davis AI to analyze workloads in Kubernetes to optimize resource utilization and reduce costs associated with running clusters.
What are some common pitfalls when configuring Kubernetes workloads? Common issues include setting overly aggressive CPU requests leading to throttling, ultimately resulting in decreased performance and increased operational costs.
Will there be future updates to Davis AI's capabilities? Yes, there are future features planned, including predictive analytics and enhanced log signal incorporation to provide deeper insights.
Where can I find more documentation about the Davis AI features? Comprehensive documentation and transparency regarding the algorithms and methodologies behind Davis AI are available on the official Dynatrace help portal.
