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There is no question that interest in data center and virtualization automation is growing -- automation frees administrators from the daily drudgery of "keeping the shop open" and allows IT professionals to focus on more strategic projects that yield long-term tangible benefits to the business. However, virtualization automation requires sound decision-making based on a comprehensive set of rules that covers every possible scenario. It's almost impossible to achieve such confidence in an environment that is always changing, so there is always a danger of poor results or unforeseen consequences in automation.
When adopting virtualization automation tools, one option is to disable automatic behavior and rely on manual approval before changes take place. For example, a tool like Microsoft's System Center allows you to select a cluster and select the "Optimize Hosts" option. This generates a set of suggested optimizations, and you can decide which optimizations to allow. This is also a good opportunity to change resource thresholds and aggressiveness settings where administrators can closely watch the ways that recommendations change in response to different parameters. It's important to document changes and results so that administrators can roll back the parameters if necessary.
Once administrators become comfortable with the tool's decision-making (especially after setting new thresholds), it's a simple matter to enable automatic optimizations and allow the tool to handle changes in response to schedules and thresholds. So the conservative approach is to start optimizations in manual mode, and then switch to automatic when the tool's behaviors are tested and well understood.
Virtualization has radically improved the utilization and efficiency of data center resources, but even a virtualized environment can be optimized. Optimizations include workload balancing -- migrating VMs to servers with the best available resources -- and powering down unneeded servers during off hours to further conserve energy and lower operational costs. Tools are emerging to tackle optimizations manually and automatically, but optimizations should be approached with careful configurations and assessments of behaviors before allowing tools to function automatically.
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