“Dynamic thresholding enabled through self-learning analytics are unique in its ability to detect abnormalities in resource utilization specific to the virtual environment,” says Bernd Harzog, The Virtualization Practice.
Boston, Massachusetts (PRWEB) July 31, 2012
VKernel, the award-winning provider of enterprise-class performance, configuration and capacity management products for virtualized data centers and cloud environments, announced today 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 Detects “Abnormal” Resource Utilization and Patterns
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.
“Dynamic thresholding enabled through self-learning analytics are unique in its ability to detect abnormalities in resource utilization specific to the virtual environment,” says Bernd Harzog, The Virtualization Practice. “The alarms can only help system administrators be more proactive in avoiding performance issues. They are now alerted when an environment is beginning to show abnormalities in resource usage that could potentially turn into performance issues and result in environment downtime.”
Dynamic Thresholding Bolsters Multi-Analytic Approach in vOPS Server Product Line
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.
“We are pleased to integrate dynamic thresholding in vOPS Server Enterprise based on the same IntelliProfile engine that has been used and improved over many years in other Quest monitoring solutions,” says Mattias Sundling, Product Evangelist and vExpert, VKernel. “Adding this capability to the multiple issue detection analytic methods that currently exist within the vOPS Server product line will allow our customers to get the most sensitive VM performance issue detection system available on the market.”
vOPS Server Enterprise 6.6.2 featuring dynamic thresholding is available now for a 45-day trial from http://www.vkernel.com. Pricing starts at $799 per socket.
VKernel, a division of Quest Software (NASDAQ: QSFT), is the number one provider of virtualization management products for virtualized data centers and cloud environments. The company’s powerful, easy-to-use and affordable products simplify the complex and critical tasks of planning, monitoring and predicting capacity utilization and bottlenecks. Used by over 50,000 system administrators, the products have proven their ability to maximize capacity utilization, reduce virtualization costs and improve application performance.