Can you afford to waste resources on a mainframe. Of course you can’t – because of your MLC - 4 Hour Rolling Average will cause your mainframe costs to go through the roof. If you’re not effectively managing your mainframe workload inefficiencies then you’re wasting money and not maximizing system availability.
Mainframes are the most cost effective platforms; assuming they are balanced and optimized. How do you know if your mainframe is optimally tuned. How do you know if your workload is tuned. After being in the business for more than 15 years, I realized – alerts are usually set incorrectly.
How are you going to increase system availability and reduce workload inefficiencies if your alerts are not set properly? Alerts are your first line of defense. A good system programmer shouldn’t have their eye-on-the-glass watching their monitoring solution. They should get an alert when something is wrong. This is why it’s imperative to have your alerts set correctly.
Does your UI looks like a Christmas tree with lots of pretty red and yellow lights on it? Or do you have the cry wolf syndrome. Cry wolf syndrome is when you choose to ignore an alert because it’s always on. Or you could have the complete opposite, which is what really scares me the most - it’s all quiet - everything is green and the storm is building up momentum but you don’t know it.
In today’s fast paced and complex mainframe environments, a good way or the only way to set alerts is by using MainView threshold advisor. It’s a self-learning analytical feature. Only MainView has, which analyzes your historical performance data and dynamically sets thresholds based on changes in during your business cycle. No matter what time of day, your alerts will be accurate, no more false alarms, no more Christmas tree lights on your screen, and your mainframe will be optimized.
Now what happens when an alert is thrown, such as high CPU utilization? MainView automatically detects the problem condition and initializes strobe data collection, reducing the need for eye-on glass attention and missing the chance to gather performance data during the high CPU utilization caused by the workload. This deep dive analysis is used to pinpoint workload inefficiencies.
This is just one example of how these solutions working together can work together to quickly, easily to increase system availability and reduce workload inefficacy.