BMC Control-M
Control-M is workload automation (traditionally called batch scheduling) software obtained by BMC Software via its 1999 acquisition of Israel's New Dimension Software. It was originally developed for IBM mainframe computers (OS/MVS operating system), but has since expanded and is also available for distributed computing platforms including Unix, Windows, Linux and OpenVMS environments.
It is designed for automation of various functions in the enterprise including traditional IBM mainframe OS/MVS (z/OS today) JCL, batch files and shell scripts, as well as routine functions such as invoking database stored procedures, invoking and using Web services and handling file transfers inside and outside the organization.
Control-M can schedule workloads on a daily, weekly or monthly interval. It can also be used to respond to trigger events such as the appearance of a file, being called by a third party application through an API, or invocation on demand through Java or Web services.
Control-M can be integrated with applications such as SAP and Oracle's PeopleSoft, to act as a scheduling organizer in these computing platforms.
With the addition of BMC Batch Discovery in 2007, it integrates with the configuration management database ("CMDB") of BMC Remedy Action Request System. This is intended to help identify the relationships between batch processes and other business and information technology ("IT") infrastructure components.
Industry recognition
The Gartner research group has regularly placed BMC Control-M in the leadership quadrant of its Magic Quadrant for Job Scheduling and moved it further into the leadership quadrant in their 2014 report.[1]
In 2010, Enterprise Management Associates's "Radar Report" on Workload Automation named BMC Control-M as the "Value Leader" with the "Best Overall ITSM Integration" and the "Best Automation Resolution." [2]
See also
References
External links
- BMC Control-M webpage
- BMC IT Service Management Solutions
- BMC Control-M Info and Documents
- VISUALjob for Control-M Workload Analytics