I have been worked on Unix and then move to Linux, then virtualized Linux Environment using VMware vSphere and now overseeing the journey to cloud-native. Technology just keeps changing and we need to adopt new technologies, make it more efficient and more profitable to the customer. Service-based IT firms and vendors are forced to evolving and innovating to stay cost-efficient and provide reliable services to the customers. IDC predicts that by 2020, 50% of the G2000 will see the majority of their business depend on their ability to create digitally-enhanced products, services, and experiences. Kubernetes has taken the lead in helping organizations deliver the cloud-native applications that drive this digitization. It gives developers the means to create better applications and services faster. But, it also means more changes and more moving parts in the environment.
Kubernetes also was known as k8s. The popular tweet about k8s is ” Here is my application, run it for me, When and where I want it, securely. that’s the end of the game.”
Scenario:
Let’s assume that customer has chosen “containers + Kubernetes” for their new application deployment. Here are some of the challenges they might face.
- How many containers required to fulfill the application demand?
- Size of the containers.
- How many containers can run on a single node?
- Should a container scale vertically or horizontally up/ down?
- Should a node scale vertically or horizontally up/ down?
- Where should a node be placed?
- How close to each other containers should be placed?
- How close to each node should be placed?
- How much underline infrastructure is required?
To resolve the above issues, we need an artificial intelligence platform which can work 24×7, “Little” or No human input required. Let’s compare process vs automate decision-making.
Process:
- Problems typically addressed after alerting via monitoring systems – Reactive
- Labour intensive.
- More data = More noise
Automate Decision:
- Problems typically addressed before alerting – Preventative
- Little or no human intervention required.
- More data = better decisions
Turbonomic – Decision Engine:
Turbonomic is a Decision Engine for IT environments. It’s a Full-stack Automation is technology agnostic and it can dig all the way down underlying infrastructure or public cloud. It takes trustworthy Actions means customers can actually automate in real-time. Maintains Compliance with IT Policies. Fastest Time to value. Proven in the world’s most complex environment.
Turbonomic collects the data using REST API’s from hypervisors, Kubernetes etc…
Have a look at how turbonomic’s full stack integration.
It’s very easy to identify the issues and take action on those.
What are the key benefits of Turbonomic?
In simple, Using Artificial Intelligence TURBONOMIC to Continuously Optimize Kubernetes Clusters.