Metafor Software Solves Server Configuration Drift - Announces Environment Anomaly Detection Engine

Boston, MA, March 28, 2013 --(PR.com)-- Metafor Software, the industry’s first provider of environment anomaly detection solutions for web and data center applications, today announced the open Beta of its SaaS based anomaly detection engine. Metafor’s advanced algorithms automate the previously time-consuming task of identifying file and package drift across hundreds of servers.

Metafor replaces manual troubleshooting with automated diagnostics to identify and prevent environment anomalies before they impact quality of service. The cloud based service can be set to check servers for anomalies hourly, daily, or only on certain days, and automatically send email alerts when servers drift from their desired state. With Metafor, performance troubleshooting and release validation can be easily performed by junior operations staff. Metafor Software installs with a single command, and provides actionable insight within minutes.

“We could have saved a week of work if we’d discovered Metafor earlier,” says Jeremy Hutchings, Technical Director at MetroLyrics. “They verify the state of our servers so we can see what’s actually deployed, not just what we think has been deployed.”

“Now I spend 4-5x less time on drift than I did before implementing Metafor Software,” says Kelcey Damage, Infrastructure Systems Architect at Backbone Technology. “Metafor's anomaly monitoring is a massive time saver.”

“Metafor provides instant actionable insight,” said Toufic Boubez, CTO, Metafor Software. “It’s a standalone solution that fills a critical gap in the DevOps troubleshooting kit by preventing drift in environments where change is continuous and constant.”

Metafor offers its anomaly detection solution for free to anyone who joins its Beta program: http://metaforsoftware.com/join-our-beta.

About Metafor Software:
Metafor Software helps IT operations detect unexpected changes and anomalies in their application and infrastructure environment so they can prevent downtime, improve performance, and reduce operational risk and failed user interactions. Unlike traditional threshold based monitoring tools, Metafor’s anomaly monitoring solution applies machine learning techniques to learn system behavior patterns and alert staff when systems start deviating from their normal state. Install Metafor with a single command, and get results in minutes. More information is available at: http://metaforsoftware.com.