“Our customers work to proactively avoid VM performance issues. Accelerated growth algorithms allow for the earliest possible detection of changes in resource usage that can turn into a performance problem," says Bryan Semple, CMO at VKernel.
Andover, Massachusetts (PRWEB) April 03, 2012
VKernel, the award-winning provider of enterprise-class capacity management and performance monitoring products for virtualized data centers and cloud environments, announced today expanded analytics with the release of vOPS Server Standard 4.7. New accelerated growth algorithms available in this update recognize unusual changes in VM performance. This capability can alert VM administrators to issues that are beginning to develop as soon as a deviation in VM performance can be detected.
VM Performance Issues Usually Caused by Shortages in Resource Availability
VM performance issues often occur when resource utilization in VMs, hosts or datastores grow to a point where no more resources are available. However, by the time this growth becomes sufficiently noticeable to a VM administrator, it may be difficult or impossible to stop a VM performance issue, especially if purchasing more hardware is the only way to avoid a problem.
Existing Analysis Methods Lack Sensitivity to Catch Accelerated Growth Early On
Existing analytic methods lack sensitivity to detect growth acceleration in resource usage. Threshold-based alarms will only alert VM administrators when resource use has grown to a warning level. Once those levels are reached, it may be too late to solve an emerging VM performance problem. Self-learning analytics may not identify accelerated resource growth as “abnormal”. This classification is highly dependent on the mathematics employed and the past performance observed in the environment. Additional analytics must also be incorporated into detection methods to determine at what time accelerated growth in resource usage will cause performance problems.
vOPS Server Standard 4.7 Detects Accelerated Growth As Soon As It Begins
Accelerated growth algorithms in vOPS Server Standard 4.7 follow the resource usage growth of 20 key VM performance metrics and continuously forecast metric levels at the existing growth rate. If this growth rate increases, the accelerated growth algorithms can trigger alarms to immediately warn of an abnormality and will importantly forecast when the resource will run out. This early detection spots issues before they reach the warning levels in a threshold-alarm, and may identify issues before a metric is classified as “abnormal” based on a self-learning algorithm. Ultimately, this advanced notice provides VM administrators with the greatest amount of time to resolve a VM performance issue before it begins to affect end users.
“The sooner an environment becomes aware of an impending issue, the more time there is to put steps in place to avoid performance issues,” says Bernd Harzog, The Virtualization Practice. “Alerting VM administrators that a resource’s usage growth has accelerated will provide early warnings signs on changes that should be investigated.”
“We are pleased to make available enhanced analytics that increase the sensitivity of our monitoring capabilities to emerging performance issues”, says Bryan Semple, Chief Marketing Officer, VKernel, “Our customers work to proactively avoid VM performance issues. Accelerated growth algorithms allow for the earliest possible detection of changes in resource usage that can turn into a performance problem.”
vOPS Server Standard 4.7 is available now for a 30-day trial from http://www.vkernel.com. Pricing starts at $549 per socket.
VKernel, an independent subsidiary 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.