Operations | Monitoring | ITSM | DevOps | Cloud

Last week, I attended the Amazon Web Services (AWS) re:Invent conference in Las Vegas, NV, with 50,000+ others. It was quite a busy week with several keynotes, announcements, and many sessions. While the hot topic at re:Invent was generative AI, I’ll focus my blog post on a few customer sessions I attended around observability: Stripe, Capital One, and McDonald’s. ‍

Today’s networks support a lot of traffic data, especially with the adoption of embedded systems. Furthermore, the complexity of the data passing through networks has significantly increased. Yet system administrators have to manage and secure these networks effectively. A network protocol analyzer is an essential component of network security and management that every administrator needs.

Network traffic analysis is an important aspect of computer network management. Organizations can gain valuable insights into the behavior of their network infrastructure, identify potential security threats, and optimize network performance by monitoring and analyzing network traffic. Several network traffic analysis tools have been developed to aid in this process, each with its own set of features and capabilities. What Is Network Traffic Analysis?

What is a Time Series Data Warehouse? How does it help in your monitoring journey? How does it differ from a Time Series Database? That and more.

Most SREs and IT Ops manage Java applications without source code access or communication with AppDev teams. When applications have performance issues those SREs or IT Ops teams deploying and maintaining the infrastructure often have to prove that it is the application at fault and supply information to the app supplier which provides evidence of the issue.

Tracing of “runnables” is a fairly new feature in Percepio Tracealyzer, added in v4.7.0. One of our automotive customers needed this feature to make ISO 26262 certification of their Electronic Control Unit (ECU) software easier. In order to properly allocate ECU functions to tasks and to cores, and to ensure that they meet the budgeted resources, it is useful to know execution times, response times and wait times for each task and runnable.

Years before founding Logz.io, I was a software engineer, working with various tools to ensure my products and services performed correctly. There were few tools I dreaded using more than application performance management (APM), and I know that I’m not alone. I hated traditional APM. It’s heavy. It’s hard to implement. It’s expensive. It takes a very long time to derive business value.

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.

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.