Operations | Monitoring | ITSM | DevOps | Cloud

This video is a comprehensive resource for understanding Mobile Device Management (MDM) and its capabilities in remotely controlling corporate devices. Features include MDM remote lock, remote wipe, remote screen sharing, and more.

Accurately predict workload growth with Galileo forecasting and modeling. Galileo keeps every original data point forever, going beyond the averages that distort reality.

Eliminate spreadsheets and manual report building. Galileo is the only platform that delivers a clear line of sight into the physical and virtual layers of AIX…and beyond. Browse our fully customizable AIX executive reports.

See every unique PID – FOREVER! Galileo tracks PID details forever across every AIX instance. You can quickly identify interactive and batch jobs, resources consumed by specific PIDs, and anomalies before they become problems.

See the complete picture. Galileo’s dynamic asset correlation allows you to navigate from an individual AIX LPAR to the Storage and SAN (or vice versa) to quickly identify the source of any issues and what is impacted.

Galileo provides the best active alerting powered by Zabbix combined with the powerful visuals from Galileo Performance Explorer.

Galileo consistently captures deep contextual operational data and stores it in perpetuity to help you identify long-term trends and “unknown unknowns." See the fiber adapter analysis performed to identify why backups are taking longer than usual for this environment.

Eliminate manual calculations. Galileo does the work for you by applying IBM’s published Relative Performance (rPerf) ratings to your performance data.

Part of the Gremlin Office Hours series: A monthly deep dive with Gremlin experts. Most Kubernetes clusters have reliability risks lurking just below the surface. You could spend hours or even days manually finding these risks, but what if someone could find them for you? With Detected Risks, Gremlin automates the work involved in finding and tracking reliability risks across your Kubernetes clusters. Surface failed Pods, mismatched image versions, missing resource definitions, and single points of failure, all without having to run a single test.