The latest News and Information on Cloud monitoring, security and related technologies.
I was speaking with a VP of Engineering friend at last year’s KubeCon about how to pitch Kubernetes to the C-Suite. The benefits for innovation were clear - containerized microservices empowered her small teams to deliver more value, more rapidly. As is often the case with Boardroom discussions, though, the question of cost was always next. Sure, they want you to innovate - as long as it’s within the constraints of a budget! But cost discussions around Kubernetes can be difficult.
To put it simply, serverless computing is a cloud computing execution model meaning that the cloud provider is dynamically managing the distribution of computer’s resources. What’s taking up valuable computing resources is the function execution. Both AWS and Azure charge more if you have a combination of allocated memory and the function execution elapse time which is rounded up to 100ms.
Minimizing costs, reducing risk, and maximizing business value—all at the same time—requires a delicate balancing act. It’s not a new challenge, nor is it unique to IT infrastructures. But when it comes to the cloud, especially in hybrid cloud scenarios, it requires you to understand the performance, risk/compliance, and cost impacts of your current resource allocations and then adjust to maintain the optimal decisions to meet your SLA and budget targets.
At Grafana Labs, we’re big fans of putting ourselves in the shoes of our customers. So when it comes to building a product, dogfooding is a term we throw around constantly. In short, what it means is that we actually use the products we create throughout their entire life cycle. And I really mean the whole life cycle.
Many of Spot’s AWS customers are using Kubernetes Operations (kops) to self-manage their Kubernetes clusters. The tool significantly simplifies cluster set up, lifecycle management via instance groups, Kubernetes Day 2 operations and generates Terraform configurations, making it a popular tool for deploying production-grade k8s clusters.