Operations | Monitoring | ITSM | DevOps | Cloud

Kubernetes vs OpenStack: which one to choose?

Kubernetes vs OpenStack is a common dilemma that organisations face when considering the modernisation of their IT infrastructure. Both are well-established open-source technologies for building cloud infrastructure, and both bring tangible benefits, especially when used in combination. Yet, they differ significantly and need to be properly bundled to feel like a fully-integrated solution. What does this mean in practice? Let’s take a look!

Kubernetes CPU Requests & Limits VS Autoscaling

In a prior blog post, we discussed the basics of Kubernetes Limits and Requests: they serve an important role to manage resources in cloud environments. In another article in the series, we discussed the Out of Memory kills and CPU throttling that can affect your cluster. But, all in all, Limits and Requests are not silver bullets for CPU management and there are cases where other alternatives might be a better option.

MIAX and Cribl Stream: Enriching Data for Improved Observability and Faster Time to Value

Using Cribl Stream for observability is a given, but what about using Cribl Stream to get MORE from your data? Observability is all about being able to collect, route, store, and search your data. Implementing enrichment with observability provides more context and elevates your ho-hum data to robust information. This is key to faster, more confident decision-making!

Exceptions Happen. Handle Them Quickly.

It’s ironic, but exceptions happen all the time. We all can relate to these in supply chain operations: the crushed package, the barcode damaged to the point it can’t be decoded, the misplaced or abandoned tote. When these errors occur, what happens? The person who discovers it is responsible for reporting it. How long does that take? Who do they report the incident to? Should they address it themselves?

What is an ESXi cluster, and how do you cluster ESXi servers

ESXi clusters involve a combination of ESXi hosts, VMware services, and vCenter to optimize load balancing, availability, and resource management for virtual machines (VMs). These clusters feature a vCenter server that centralizes the management process to facilitate shared resources that drive higher availability, scalability, and load-balancing capabilities.