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Loom v2.1: OpenStack Monitoring, Advanced Root Cause Analysis, and New Correlation View

We are excited to announce that Loom 2.1 is now availableavailable. As we gradually deploy this version to all of our customers, we want to take this opportunity to elaborate on and emphasize some of the key new features:

Loom for OpenStack Monitoring

OpenStack is an open-source software platform used to develop private &amp; public cloud environments. It consists of multiple, interdependent microservices that together provide a production-ready IaaS layer for applications and virtual machines. OpenStack is increasingly becoming an integral part of many organization's cloud environment, and Loom 2.1 boasts an OpenStack-specific integration. Integrating with OpenStack was a natural application of Loom's
capabilities and makes monitoring your OpenStack logs easier than ever.

Watch this 2-min video to realize the benefits of Loom v2.1 for your OpenStack monitoring efforts:




Business Dashboard (closed-beta)

While our data feed expertly details and analyzes incidents and alerts, we wanted to give customers a more holistic view based on feedback we have received. With the update, our dashboard presents a general health score of your application based on many factors, including the rate and severity of service incidents, the quality of data flow, and other metrics. It gives a summarized view of your open events, the overall error rate, and the general movement of data to Loom.

This new view provides a more comprehensive picture of what's happening in your environment, helping you focus quickly on what's most critical. 



While this version has pre-defined configurations, future releases will allow customers to choose specific metrics using configurable widgets.

Distributed Entity Tracing

We take great pride in Loom's "human-like" approach to log analysis and "Entity Tracing" is the newest expression of it. In many cases, when faced with an alert from one service or application, IT Ops will analyze the incident by tracing the behavior of an entity throughout the chain of dependent applications. Eventually, he or she will be able to identify the exact request and moment when things started to go wrong and expose the root cause of the incident.


Loom aspires to not only automate root cause analysis but also to empower it with machine learning. Once Loom detects an event, the platform will apply its algorithm to extract each meaningful entity.  Loom will then thoroughly search your stack for "prime suspects," for example a User, Path, IP or Host that are connected to the incident no matter the service or application. Once found, Loom will present them in chronological order, effectively conducting the root cause analysis for you.




Entity Journey-414696-edited.png



New Correlation View

We are focused on making Loom as intuitive to use as possible, both in the "human-like" way it analyzes data and in the easy-to-understand why it displays data. With Loom 2.0, we are introducing a new dashboard for viewing correlations that is more in line with how IT Ops teams think.  


Just as a reminder, a correlation is an incident composed of multiple, related alerts from different services and applications across your IT stack. To conduct correlation, Loom uses several algorithms in conjunction, including time and anomaly analyses. Loom also uses metadata from alerts to correlate incidents based on their commonalities.

The new view abundantly demonstrates the logs Loom used to establish the correlation, helping you to understand the nature of events fully. We also changed the feed to be much cleaner while still signaling cross-application incidents.





Giving thanks where thanks is due

Announcing new features is always fun, but it gives us special pleasure to use this space to acknowledge one of our most renowned users, Dani Rivkin. Dani has always been a great contributor to Loom. His insights and ideas helped us a lot along the way to bringing real value to our customers everywhere. Thank you, Dani. You rock!




Loom Systems delivers an AIOps-powered log analytics solution, Sophie, to predict and prevent problems in the digital business. Loom collects logs and metrics from the entire IT stack, continually monitors them, and gives a heads-up when something is likely to deviate from the norm. When it does, Loom sends out an alert and recommended resolution so DevOps and IT managers can proactively attend to the issue before anything goes down.
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