App containerization has picked up momentum in recent years as it helps to save costs, time and resources. Resource vendors are available to assist in containerization of one or two apps, but most vendors struggle to execute a large number of app containerization projects with the same skill level and outcomes. As a matter of fact, it’s a manual approach in certain scenarios with a lot of dependencies which is not only time and cost consuming but also prone to errors and miscalculations.

Event and alert severity are extremly important information for an effective alert management and response. Severity information determine the speed of response, needed resource allocation and the action path taken. Naturally, critical alerts have higher priority than major alerts which again overrule minor alerts.

When talking about log management, search history is overlooked more often than not. Past searches can be used as part of log analysis and forensic analysis, but the main issue with this data is the speed of search which gets compromised as data volume gets greater. We will discuss some ways to get the best out of your saved searches and to speed up the search process.

As with any enterprise technology, there are benefits and challenges when creating IT environments in the public cloud. The benefits include cost savings and the ability to easily scale up and down (just to name a few).

Serverless development opens lots of new opportunities, and if you’re invested in serverless (or you’ve been following the hype) you’ll know that cost efficiency is principal among those benefits. Simply put, we can save money by choosing the right tool for the right task. Since a distributed microservices architecture is made up of many managed services it’s a simple task to change out the building blocks of a particular application flow.

Logs from a variety of different AWS services can be stored in S3 buckets, like S3 server access logs, ELB access logs, CloudWatch logs, and VPC flow logs. S3 server access logs, for example, provide detailed records for the requests that are made to a bucket. This is very useful information, but unfortunately, AWS creates multiple .txt files for multiple operations, making it difficult to see exactly what operations are recorded in the log files without opening every single .txt file separately.

Elasticsearch and the rest of the Elastic Stack are commonly used for log and metric aggregation in various environments, including Kubernetes. In addition, the Elastic Stack is frequently being used for uptime tracking, with Heartbeat, as well as Application Performance Monitoring (APM), with agents supporting common programming languages, including Java.

This article originally appeared in TechBeacon. Gartner first coined the term "AIOps" a few years ago to describe "artificial intelligence for IT operations," and over the last few years, IT operations monitoring tool vendors have begun incorporating AIOps features into their products. Now AIOps tools are commonplace, but many IT leaders remain cautious about using these relatively new capabilities.

Standard operating procedures—more commonly known as SOPs—are written, step-by-step instructions that describe how to perform a routine activity. While you can create an SOP for anything, SOPs that outline technical procedures are a well-known path to increasing efficiency in your MSP business. Whether you’ve got existing SOPs you’re interested in updating, or you’re looking for some basic steps to build brand new SOPs around, you’ve come to the right place.

January is a good time for bug-fixing and we've spent the last couple of weeks making small tweaks and improvements to Downtime Monkey. We've focused on the kind of bugs that aren't urgent but shouldn't be overlooked and aim to continue this for a couple more weeks before starting development of the next major feature. We'll document each change in its own post. First up is an improvement to the range of webpages available for monitoring: webpages with query strings can now be monitored...