Effective and efficient CPU usage is one of the strategic assets of an organization. Especially depending on the CPU numbers and the clock speed, how fast you can handle a certain amount of data is directly related and aswell affecting organization’s efficiency. If you have enough CPU power, fast reporting, fast working business softwares and applications, fast communication softwares will put you a step ahead of your rivals.
So much technology regarded as academic issues in the beginning of 2000 today become ordinary issues of our daily life. Such technologies like distributed architecture, parallel computing methods etc. are especially developed for organizations to have more efficient usage of CPU and to achieve faster results. Therefore it is important for organizations to know their CPU power and when and how dense it is used.
In today’s IT surveillance softwares for each machine CPU usages are independently tracked and CPU usage density is calculated based on parameters like “CPU Utilization” or “CPU Queue Length”. Although this is an important practice but it is not enough.Bu önemli bir pratik olmakla birlikte yeterli değildir. Distributed/Cluster/Parallel calculation methods now enable software and applications run in parallel and jobs are done in different machines by different CPUs or Cores.
Lets assume that in Bank X core banking app running on a system designed to run on 25 Servers and each Server has 4 CPU. We can assume as well that Bank has 1 virtual machine with 100 CPU. So in that case it is crucial to calculate CPU usage trends for each machine and in overall for an effective capacity planning. And looking every month/year both micro and macro levels of the system we can guess how much of CPU power is being used and how many years we can keep going with our existing systems.
For more complicated situations, 20 of these 100 CPU can run a module and the rest other modules. In this case we can have different combinations of 100. Complicated huh? Different small virtual machines can make our each and every virtual machine. Each group has to be followed up and managed the same way. Thus it is possible to observe 20-25 CPU averaged (may be a couple of physical machines) virtual machines and and calculate their performance and capacity metrics, trend analysys.
How can we achieve such a system? With Bussion it is possible to connect the systems via WMI or SSH connectors and directly retrieving data from the systems then creating widgets.
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