Xangati's Latest VM Monitoring Appliance Introduces Next-Generation Remediation Capabilities


Printer-friendly version Email this CRN article

Xangati began its journey toward next-generation IT event management Monday by releasing software capable of automatically remediating resource contention problems.  

The updated Xangati Virtual Appliance -- which employs machine learning to ensure resources remain properly allocated to workloads running on virtual machines -- allows system administrators and MSPs to approve automated implementation of prescriptive actions in VMware environments.

While the latest release adds the capability only for vSphere, the San Jose, Calif.-based developer of workload performance solutions will, in the coming months, introduce automated remediation to multiple environments it supports, according to the company.

 [Related: Xangati, Seeking Greater Growth, Takes A Major Turn Toward The Channel]

The maturity of the entire category is undergoing a major transition, Atchison Frazer, Xangati's vice president of marketing, told CRN. Next-generation solutions are being driven by "a lot of customer pain with the amount of time it takes to resolve performance degradation."

"It's a strange time. This whole category is moving," Frazer said. "It's stuck between monitoring 1.X and 2.0."

Xangati's previous platform, while offering root cause analysis and prescriptive remediation suggestions, was still passive.

"An administrator sits in front of the dashboard, overlays Xangati's 'Storm Tracker' utility to the data mesh, which allows getting to the root cause of any problem and solving it quickly," he said.

But the ultimate solution still required human tinkering -- which meant a fair amount of latency before problems were solved.

"The tool generates very prescriptive recommendations when dynamic thresholds kick in," Frazer said. "But you, as a human, would have to go move a virtual machine around, add more memory, add CPU power, kill a bad actor, or deal with an end user."

Many solution providers see that model as involving "too much human-productivity training for me to get interested in building a practice around," Frazer told CRN.

Printer-friendly version Email this CRN article