Following the release of Azure Monitor for VMs in January 2020, some changes have been made to how its performance data is stored in Log Analytics. The most significant of these changes is that this data is now stored in the InsightsMetrics table rather than in the Perf table, as we discussed in our ‘What is Azure VM Insights?’ article.

There are different tools and mechanisms for autoscaling in Kubernetes at both the application and infrastructure layers to help users manage their cluster resources. In this article, we’ll explore two infrastructure autoscaling tools for Kubernetes — Ocean by Spot and open source Cluster Autoscaler.

Version 2.1 of the HAProxy Data Plane API expands support to all available request and response actions, adds Lua actions, and improves file handling. A year ago, we introduced version 1.0 of the HAProxy Data Plane API, enabling you to configure your HAProxy load balancers remotely through a modern RESTful HTTP API. That first version of the API focused on the essential behaviors for creating frontend proxies, backend server pools, ACLs and traffic switching rules.

Different IT organizations have different needs. The one-man shop might find the best success with open-source software, while enterprises often need something a little more. But occasionally you’ll see an enterprise using open-source or something designed for a small to medium-sized business. This can be a good thing in certain instances, though it’s not without risks. So, why might you want to use SMB or open-source gear in an enterprise setting, and when might it be a good thing?

As all Papertrail fans know, sending logs to Papertrail using syslog is quick and easy. Generating and transmitting syslog packets usually involves just 2 – 4 lines of code, and you can see your logs flowing into Papertrail in minutes. There are times, however, when you just can’t use syslog or install a remote_syslog2 daemon. This is where the new support for sending logs via HTTP comes in. And the best news is, it’s just as quick and easy to set up.

Talk about performance monitoring to any system admin or IT manager and one of the first questions they will ask is whether the monitoring is agent-based or agentless. The moment you hear that question, you know that they are interested in an agentless monitoring solution. Such is the fear of having agents on critical servers in the infrastructure! In this article, we will discuss.

Edge computing – with its ability to help unleash the potential of smart cities and autonomous vehicles, is something often thought to exist “around the corner” as opposed to an industry already in motion.

This is a review of the last three years that we spent stabilizing Marathon. Marathon is the central workload scheduler in DC/OS. Most of the time when you launch an app or a service on DC/OS, it is Marathon that starts it on top of Apache Mesos. Mesos manages the compute and storage resources and Marathon orchestrates the workload. We sometimes dub it the “init.d of DC/OS”. Being such an integral part of DC/OS, we must ensure that it keeps functioning.

When was the last time you had the chance to listen to some of the most beautiful concerts that nature can play for you? From simple chirps and tweets to complex bird songs composed into a sophisticated soundscape, you may wish you could decrypt and understand their daily conversation. “Hey, good morning, how are you today?”, you might hear in the early hours, sometimes so loudly that you are awakened from the chirping.

This is part of a series of articles discussing strategies to implement serverless architectural design patterns. We continue to follow this literature review. Although we use AWS serverless services to illustrate concepts, they can be applied in different cloud providers. In the previous article (Part 1) we covered the Aggregator and Data Lake patterns. In today’s article, we’ll continue in the Orchestration & Aggregation category covering the Fan-in/Fan-out and Queue-based load leveling.