Operations | Monitoring | ITSM | DevOps | Cloud

As applications in the cloud become more distributed and complex, the Mean Time To Resolution (MTTR) for production issues is getting longer. Modern systems are built with hundreds of distinct, ephemeral, and interconnected cloud components, which can make it exceptionally hard for engineers to understand the current state of their applications, what problems are impacting customers, and why those problems are occurring.

Reporting from multiple engineering systems is crucial for maintaining the high velocity and quality standards required in today’s tech industry. However, creating reports can be challenging due to the need for deep technical expertise and aggregation of many separate systems worth of data.

In a previous blog post, we explained how containers’ CPU and memory requests can affect how they are scheduled. We also introduced some of the effects CPU and memory limits can have on applications, assuming that CPU limits were enforced by the Completely Fair Scheduler (CFS) quota. In this post, we are going to dive a bit deeper into CPU and share some general recommendations for specifying CPU requests and limits.

Tradeoff: a balance achieved between two desirable but incompatible features; a compromise. Schooling often promotes the idea that there is a right and wrong answer to questions… It does little to prepare us for how many times that there are multiple right answers and no definitive best path forward. In a time where we have unlimited information at our fingertips, you can throw a stone and hit a thousand people with an opinion.

Backstage, a development portal, allows developers to maintain constant vigilance over the health of their networks and services, no matter where they are deployed. This is invaluable to teams, as many different deployments across different environments need to be monitored to ensure security and compliance. Find out how to evaluate, implement, and succeed with Backstage in order to maintain your security perimeter and ensure that all deployments are working as intended.

For extremely high throughput web applications, it is important to load balance the traffic across multiple servers. However, load balancing the traffic alone is not enough at times. The reverse proxy server that handles the workload needs to be performant, too. In our previous article, we discussed the NGINX reverse proxy server and understood how to monitor it. In this article, we set up monitoring for an even more performant reverse proxy server - HAProxy.

Engineering and ITOps teams have an important mission: keeping their software and digital systems performing and reliable. But as we’re about to embrace a new year full of changes, industry shifts, and AI developments, this mission is challenged by increasingly complex environments, technology alternatives, and an overwhelming number of tools available. The result? Overages, tool sprawl, and toil, which all lead to longer times to detect and resolve issues.