Virtual machines (VMs) have been around for quite some time now and have become a cornerstone of modern-day IT infrastructure. They provide a convenient way to run multiple operating systems on a single physical machine, allowing users to consolidate their hardware and save costs. However, as VM technology has evolved, newer versions of VMs have been introduced, such as generation 1 and generation 2 VMs.
As a Senior Product Manager at Mezmo, I understand the challenges businesses face in managing data complexity and the higher costs that come with it. The explosion of data in the digital age has made it difficult for IT operations teams to control this data and deliver it across teams to serve a range of use cases, from troubleshooting issues in development to responding quickly to security threats and beyond.
Welcome to this three-part mini-series on real-time Linux. In Part I, we set the stage for the remainder of the series by defining a real-time system, and went through common misconceptions. We also covered the broad market applications of a real-time Linux kernel. Several applications across a wide range of use cases and verticals require real-time computing capabilities. Sectors like industrial automation, energy and transportation have strict precision requirements for their systems.
Would you believe us if we say that the term “remote management” was once a nightmare for conservative or old-school managers? No matter how much it seems to be hyped today, there was a time when remote management was considered a nightmare for several dated enterprise owners across the globe. After the massive COVID pandemic hit, the concept of remote working seems to have leveled up. Did we mention this is not going to be easy though?
As questions and challenges loom over the tech industry and the larger economy, now is a perfect time for us to take a step back and learn from the past. As reliability engineers, we regularly use Service Level Objectives (SLOs) to understand the performance, reliability, and trends of our systems to help inform and prioritize our decision making.
AWS Marketplace + FireHydrant: your path to easier compliance, consolidated spend, and faster procurement.
StormForge Optimize Live is a machine learning-powered performance and resource optimization solution for Kubernetes workloads. Optimize Live ingests and analyzes production observability data and recommends specific actions to optimize CPU and memory utilization. You can take these actions manually or set them to occur automatically, making it easier to maintain a high level of application performance while minimizing cloud costs.
The current popularity of Redis is well deserved; it’s one of the best caching engines available and it addresses numerous use cases – including distributed locking, geospatial indexing, rate limiting, and more. Redis is so widely used today that many major cloud providers, including The Big 3 — offer it as one of their managed services. In this article, we’ll look at how to monitor Redis performance using Prometheus, the similarly popular open-source monitoring system.
