Operations | Monitoring | ITSM | DevOps | Cloud

In this article we are going to consider the two most common methods for Autoscaling in EKS cluster: The Horizontal Pod Autoscaler or HPA is a Kubernetes component that automatically scales your service based on metrics such as CPU utilization or others, as defined through the Kubernetes metric server. The HPA scales the pods in either a deployment or replica set, and is implemented as a Kubernetes API resource and a controller.

Monitoring your Azure cloud computing environment requires an understanding of each component of your Azure infrastructure and how they all interact. Cloud computing with Azure offers each of the three main advantages of cloud service providers: infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). Combined, these three services provide a comprehensive cloud-based computing setup for any business.

At Elastic we are constantly innovating and releasing new features. As we release new features we are also working to make sure that they are tested, solid, and reliable — and sometimes we do find bugs or other issues. While testing a new feature we discovered a Linux kernel bug affecting SSD disks on certain Linux kernels. In this blog article we cover the story around the investigation and how it involved a great collaboration with two close partners, Google Cloud and Canonical.

AWS Lambda enables you to run serverless functions in the AWS cloud, by manually triggering functions or by creating trigger events. To ensure your Lambda functions are running smoothly, you can monitor metrics that measure performance, invocations, and concurrencies. However, even if you continuously monitor, once in a while you are going to run into what’s termed a Lamba cold start. There are various ways to prevent AWS Lambda cold starts.

Since almost the beginning of programming, the idea of write-once and deploy everywhere, on all platforms, has been an unreachable ideal to minimize development costs for cross-platform applications, drive UI consistency and reduce security service area. In programming, the cross-platform languages Java and Python have topped developer utilization charts for decades.

Some, maybe. But not most. Look, I have this theory. I also have data. And as with most stories in my life, it all starts with a trip to Las Vegas. In 2017, I attended my first AWS re:Invent in Las Vegas. For a while I heard how re:Invent was either a “cloud” or a “developer” event. I now believe neither to be true. Most people I met at re:Invent were operations folks. They were there to learn about monitoring and managing applications running in AWS.

I’m a bit of a data nerd. And like many fortunate people, I’m still working from and generally staying home and have a lot spare time on my hands. So what does that combination result in? Graphs!

Two years ago we introduced Splunk Insights for AWS Cloud Monitoring and Splunk Insights for Infrastructure on the AWS Marketplace as a Pay-As-You-Go Amazon Machine Image, where you could initiate an instance and pay hourly to use these products after a 15-day trial. Assessing our portfolio, we are discontinuing these offerings to focus on differentiating capabilities, namely the ability to search and apply machine learning to your data in addition to visualizing insights.

Amazon Elastic Block Store (EBS) provides block storage for applications that are running in the cloud. However, not every company is getting the most out of the EBS volumes they are using. Some companies can pay too much for EBS volumes without utilizing the allocated storage and IOPS. Other organizations may pay high prices because they are using the wrong disk type for their needs. This article explains five techniques you can use to optimize the performance of your EBS workloads.