VKernel Adds Self-Learning Analytics
July 31, 2012
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VKernel announced the addition of self-learning analytics to its existing analytics feature set.

This new capability allows virtual administrators to deploy dynamic thresholding to detect even more existing and emerging VM performance issues such as abnormalities in CPU, memory and storage utilization.

Dynamic thresholding makes use of self-learning analytics to understand the “normal” range of VM resource usage in a virtual environment. Because every environment can be vastly different, these analytics observe consumption of resources over a period of time to understand usage.

For example, if a VM displays high CPU utilization on the same day each week, these analytics “learn” that this is a usual occurrence and will consider this to be the baseline utilization, dynamically setting warning thresholds differently for this day. As a result, this VM would be considered to have a high CPU utilization performance issue only if the CPU utilization is vastly higher than usual for this specific day of the week.

Through this method, “abnormal” behavior for resource usage is dynamically determined and false positives can be removed for behavior that is shown to be typical.

While dynamic thresholding based on self-learning analytics is valuable for analyzing virtual environments, multiple analytic types are required to detect all sorts of virtualization issues. Because dynamically set thresholds are specific to each VM’s observed resource usage, issues that exist while a baseline is being established will not be considered problematic.

Additionally, many issues cannot be detected with dynamic thresholds, such as memory swapping, accelerated storage utilization and high disk latency as they require metric-specific static threshold alarms. Virtualization management systems which rely solely on dynamic thresholding will be unable to detect these and many other kinds of issues. VKernel’s approach is to build and deploy the right types of algorithms to maximize accurate analysis of virtual environments.

With vOPS Server Enterprise 6.6.2’s new feature set, dynamic thresholding adds precision in determining which resource usage patterns are normal or abnormal, in VM CPU, memory, storage and disk I/O utilization. This is in addition to other analytic types existing within the vOPS product to detect VM performance issues.

vOPS Server Enterprise 6.6.2 features dynamic thresholding for VM resource utilization by enabling the IntelliProfile self-learning analytics engine. IntelliProfile is a mature technology featured in other Quest products to detect abnormalities in usage in applications such as Microsoft SQL Server. Dynamic thresholding will complement existing analytic types within vOPS Server Enterprise such as threshold-based alarms and accelerated growth alarms to expand the total number of issue types that can be detected by the vOPS Server product line.

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