The adoption of container orchestration platforms is accelerating exponentially in organizational IT and Kubernetes leads the pack in both spending velocity and market share. Containers, as flexible as they are, bring in various new performance challenges that confront consistent and predictable application performance. This is because things are constantly changing and developers have to wade through a sea of observability data which ends up taxing resources and killing efficiency. The result is that organizations end up throwing unnecessary resources at the problem hoping something would work. This is where StormForge comes in and takes on these challenges head-on by leveraging in machine learning.
StormForge – a brief
Say that you are being told beforehand what an optimal configuration and resource allocation should be for a Kubernetes environment. Now, remember that Kubernetes is a rapidly changing environment. So, what happens to the predetermined optimal configuration and resource allocation when something changes? StormForge can use any metric you care about and work with any configuration to address this problem specifically. It abstracts away the complexities that come with new technologies and provides IT professionals and developers the opportunity to concentrate on innovation. StormForge’s working principle is to empower the developer in the process, instead of automating them out of it. It helps organizations simplify the process of finding an optimal balance between performance and cost through machine learning.
What problem is StormForge solving?
StormForge takes the raw telemetry data and monitors the performance of your application. It, then, uses machine learning algorithms to tell you the exact configuration you need to use for your application to achieve the results you want without over-provisioning. StormForge optimizes deployment as well as the application.
StormForge views the things that happen in the pre-production environment as the experimentation phase. Machine learning, then, allows a user to experiment and design scenarios. When the production piece is added to this, you get to run the required checks and balances between the two environments to ensure your deployment is aligning with your SLOs and your business objectives.
5 Killer Features of StormForge you didn’t know you needed
1. Uniqueness
StormForge is capable of optimizing applications without necessarily knowing them, just as long as you do. The thresholds and business objectives in every enterprise across their different ecosystem of applications are different. These cannot and should not be predefined. Business objectives are treated as inputs from the application team addressed by StormForge in an application over application manner. StormForge enables organizations and developers to build specific thresholds and allows machine learning to read and send recommendations within those bounds.
2. Beyond observability
Although StormForge gets boxed into the Application Performance Monitoring category quite frequently, it is really more of an intelligence platform. StormForge is able to draw insights and intelligence because of the core machine learning it has built over the years. It acts as a tool that enables users to seamlessly integrate StormForge into the tools and investments they already have in place. It takes reactive APM and observability insights and adds a proactive element to it. StormForge, essentially, is an AI Ops platform.
StormForge’s product, Optimize Live, has the ability to work with any and all cloud-native environments built on any CNCF-certified Kubernetes distribution. What’s more, it leverages data from existing observability solutions and runs it through its machine learning. This enables Optimize Live to come up with recommendations that can improve and optimize your Kubernetes settings.
3. ML is not just a buzzword
Machine learning gets tagged to a lot of things as it tends to be a buzzword and looks good. When it comes to StormForge, machine learning is the foundation it was built on. The platform claims to provide machine learning powered multi-dimensional optimization and this claim is more than just an overstatement.
The problem of managing Kubernetes workloads in a resource efficient way is most definitely customized for the use of machine learning – the core of StormForge purposes.
4. Optimizes interaction with old hybrid applications
StormForge has the ability to offer performance testing in lower order environments like old applications that weren’t engineered as well as the current ones. This is because StormForge can use any metric you care about and work with any configuration for an application. As long as a metric you want to concentrate on for an application can be tracked, StormForge can optimize against it.
5. Pre-prod and prod combo pack
Optimize Pro and Ptimize Live, the two major products released by StormForge, combine to complement each other and provide optimization for pre-production and production environments. The former provides experimentation-based optimization for pre-prod environments and the latter provides observation-based optimization for production environments. Together, they create a platform that solves the complications faced by organizations on day two of their cloud native operations.
Conclusion
StormForge has the ability to adapt itself to completely new data structures. And in this lies its longevity. StormForge is data agnostic and doesn’t get caught up on data structure issues. This means that as long as you can capture data through any series of input like telemetry, observability, etc., and provide StormForge access to it, StormForge can come up with the perfect configuration. This holds true for any application, regardless of what the traffic pattern looks like as StormForge can optimize for any and all kinds of metrics. It sure does look like StormForge is here to stay for a long while.
If you have questions related to this topic, feel free to book a meeting with one of our solutions experts, mail to sales@amazic.com.
