User experience is the biggest and most important factor in determining the success of Citrix rollout in an organization. When end-users are happy with their virtualized applications and desktops, then everything is hunky dory and Citrix admins can focus on operations and maintenance.

Developers are building and deploying to production with greater frequency. Elite organizations are deploying to production multiple times per day. All the while we continue to distribute our applications even wider with the adoption of micro-services, and global deployments. This consistent churn and increasing code complexity create the perfect storm that makes finding problems even harder. How do you know the changes just committed actually deployed? How do you know the changes worked?

The introduction of e-commerce stores has made life so easy for the people. It does not make a difference if you are the consumer or a seller. For a seller, it provides the opportunity to express the worth of their brand and product(s). For a consumer, it gives them an all in one platform, where they can shop for multiple categories. With this, the most significant ease for both parties is to opt for e-commerce business is that you can do all this without taking a step out of their house.

In this tutorial, we’ll use the BIRCH (balanced iterative reducing and clustering using hierarchies) algorithm from scikit-learn with the ADTK (Anomaly Detection Tool Kit) package to detect anomalous CPU behavior. We’ll use the InfluxDB 2.0 Python Client to query our data in InfluxDB 2.0 and return it as a Pandas DataFrame. This tutorial assumes that you have InfluxDB and Telegraf installed and configured on your local machine to gather CPU stats.

The easiest way to get started with Icinga is a single-node installation. If you are new to the Icinga world, here you have a kickstart for installing Icinga 2, Icinga Web 2 and Icinga Director on CentOS 7. With these steps you will have a ready Icinga environment for monitoring your infrastructure.

Ever experienced bad logging- whether it’s the wrong log, the wrong information, or a multitude of other logging woes? We aren’t able to count the number of times anymore that we’ve happily gone and set log lines, only to find out that it was all for naught. The frustrations are endless. What is meant to be magic for your code, the ultimate savior when debugging, has become the ultimate frustration.

If you’ve been following the U.K. healthcare landscape, you would know that the country has been considering replacing pagers for the longest time. This may soon materialize, partly accelerated by the challenges that doctors are facing during the COVID-19 pandemic. The pager replacement initiative not only signifies a pivotal shift from the aging infrastructure, but it also indicates how pagers have failed to thrive in today’s unprecedented times.

With the difficulties we’re facing during this time, it can be difficult to keep up with the increasingly vast demand for our services. You need to make use of all the tools in your toolbelt in order to conserve your team’s cognitive resources. Two ways you can do this are through automating toil from your processes and prioritizing with SLOs.

In the second part of our "SLOs for AWS-based infrastructure" blog , Gigi Sayfan dives deeper into understanding the landscape of AWS compute by using the lens of Kubernetes to compare and contrast & covers in detail setting of SLOs for ECS, EKS, Fargate, and Lambda based services.

Isn’t all logging pretty much the same? Logs appear by default, like magic, without any further intervention by teams other than simply starting a system… right? While logging may seem like simple magic, there’s a lot to consider. Logs don’t just automatically appear for all levels of your architecture, and any logs that do automatically appear probably don’t have all of the details that you need to successfully understand what a system is doing.