Virtualization benefits the data center by increasing the utilization of computing resources that might otherwise be wasted. For example, a traditional physical server with a single workload might only use 10-15% of the server’s processor cycles or memory space -- essentially wasting the remaining 85-90%. By applying a virtualization layer to the server, multiple virtual machines (VMs) can reside on the same server and each consume a portion of the available physical resources. It’s not uncommon for a virtualized server to host 10, 15, 20 or more VMs (depending on the resource demands for each VM). Thus, the same amount of computing work can be performed with far fewer servers, reducing the cost and space demands for physical systems while reducing power and cooling requirements.
The principal challenge with virtualization is that resource use is not constant throughout the day, month or year. Many workloads experience fluctuations in resource needs as the number of users change, the type of tasks required at the time and so on. For example, a corporation might provide an important application to its employees, but if the employees are only using the application from 8 a.m. to 5 p.m., the workload is idle (and unneeded) the rest of the day. Another example might be a payroll application that only processes payroll data a total of one or two days each month. These circumstances also represent wasted computing resources within the virtualized data center, and organizations can reduce this waste by adjusting resources and migrating workloads as usage patterns change.
Consider the previous example of the important business application. If it were possible to reduce the resources allocated to the idle VM, more resources would be made available for other workloads that might need them -- or the deprecated workload could be migrated (parked) on a highly consolidated server where it could handle a low volume of work during off hours, and then re-migrated and re-adjusted in preparation for the new day. The payroll workload might even be shut down and saved to the storage area network until it’s needed for another payroll cycle. All of these tactics further conserve server resources and make the most of existing computing.
It is certainly possible to adjust the resources provided to each VM -- or consolidate less-used workloads to secondary servers (or park them entirely) until they’re needed again -- but those processes have typically required manual intervention from IT administrators. It’s impractical for any administrator or staff to constantly assess resource usage and adjust resources or migrate VMs on-the-fly.
However, a new generation of software tools is emerging to automate some of these resource optimization tasks. One example is Microsoft System Center, which can recommend VM migration when resource demands pass preset levels -- migrating the VM (often automatically) to another server that is better equipped to handle the workload’s demands. System Center also provides power optimization features that can automatically power down or power up nodes within a server group in response to computing activity. For example, suppose server A is only running at 20% processor utilization and server B is running at 30% processor utilization. Server A can move its workloads to server B and server A can power down (server B would then be running at 50% processor utilization). While server A is powered down, there is almost no energy use and additional savings for the enterprise.
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