Imagine it’s 3 AM, you’ve just been paged for a critical issue- queues filling up quickly, and you don’t know why. You turn to logs, looking for something abnormal, a change that could explain what is happening so you can fix it. Sound familiar? Unfortunately, searching through logs to uncover changes is a time-consuming process.

Get Better Observability With Machine Learning Anomaly Detection. DevOps teams today are challenged with the rapid growth and complexity of infrastructure. Managing those environments only through static thresholds becomes insufficient, so to address this issue, modern DevOps teams rely on advanced ML/AI algorithms.

Have you ever been paged for a critical issue and started troubleshooting only to find an obvious drop in requests that weren’t caught by a static threshold? Or a significant increase in a metric that didn’t cross a static threshold? Or even, evidence of warning alerts triggered long ago that should have enabled someone to resolve the issue and prevent it from causing business impact, but instead was ignored in the massive alert volume received by the team?

Reacting to alerts can be a pain, however, there are ways to be proactive and decrease frustration concerning IT Alerting. Developing an alerting strategy saves IT Operations and Development teams time, money, and eliminates notifications from low priority alerts. Keep reading for more information on routing and escalation chains, fielding alerts, and how to communicate an alerting strategy to management.

Fully taking advantage of cloud infrastructure includes the ability to scale up and down dynamically, taking the need and load off your services. The compute services like Amazon Web Services (AWS) EC2, Azure Virtual Machines (VM), and Google Cloud Platform (GCP) Compute Engine allow Auto Scaling of the instances of the service. This helps manage the responsiveness and costs of your cloud services by ensuring that the instance counts go up and down depending on demand.

Automating client onboarding can eliminate the tedious task of cloning dashboards, creating group directory structure, setting up reports, and configuring access roles. All of these tasks are prone to human error and to put it mildly, not really fun to do. In this blog, we’ll walk through a PowerShell script to automate some of these tasks.

2020 has been a year of challenges, and across all industries, companies are working hard and fast to remain efficient in the face of a new normal. Now that hiring freezes are slowly thawing out, many companies are starting to hire new people virtually and want to create remote cohesion between new and existing teammates. The lack of physical proximity means your team will need to ramp up on communication, transparency, and accountability.

When it comes to “business as usual,” the workflow of IT teams today likely looks quite different than it did even a few months ago. Because of this, being able to adapt and put new safeguards in place that ensure uninterrupted business operations is more important than ever. In fact, it’s a necessity.

At LogicMonitor, we are continuously improving our platform with regards to performance and scalability. One of the key features of the LogicMonitor platform is the capability of post-processing the data returned by monitored systems using data not available in the raw output, i.e. complex datapoints. As complex datapoints are computed by LogicMonitor itself after raw data collection, it is one of the most computationally intensive parts of LogicMonitor’s metrics processing pipeline.