Mainframes are often called ‘white elephants’ not because they consume lot of resources but they run several processes and handle huge data volumes effortlessly. Even today, so many companies that require lot of number crunching are highly dependent on them. However, running a mainframe is costly. It is not what this blog says but the manufacturers of mainframes themselves made this statement. For example, the cost of purchasing the hardware and software for every additional MIPS [Million Instructions Per Second] is huge. Because of this, the owners of mainframes are looking for alternate ways, which helps them to reduce the cost factor of adding MIPS. Running predictive application on the mainframe data helps companies understand where they have to look, tweak, and trim running time of processes to keep the running costs of the mainframes under a manageable level.
Following are a few alternate ways that are run by the mainframe owners to keep the running costs of their mainframes low:
- Reduce costs by looking for inefficient applications and code that use excessive CPU time
- Monitor applications as they run in real-time and identify where the problems are, if any
- Remove unnecessary functionality from systems software to reduce monitoring processes
- Reduce the peak loads on the mainframe by deferring certain batch workloads [such as backups and stock consolidation] to quiet periods
- Reduce overall processing requirements through good tuning of mainframe
In summary, companies can optimize the way mainframes deal with their respective tasks by going through the analytics data, fine-tuning mainframes based on that data, and removing or deferring certain unwanted, unnecessary, unimportant functionalities to quiet periods.