SIOS Technology today announced the newest version of its SIOS iQ IT analytics platform, which harnesses the power of machine learning and deep learning analytics to optimize the performance and efficiency of VMware environments. A new flexible, API-driven integration architecture enables SIOS iQ to integrate data from a range of sources, including application monitoring tools and data aggregation fabrics such as Splunk, Hadoop and Elasticsearch. Providing a more comprehensive view of the IT infrastructure, SIOS iQ empowers IT to automatically and instantaneously identify and correct the root causes of application performance issues and to predict future application performance with unparalleled precision and ease-of-use.

As IT teams move more business-critical applications into virtual environments, they are becoming increasingly overwhelmed with reactive firefighting of performance issues. They are still working and problem-solving in operational IT silos (network, computer, storage, and application) and using threshold-based tools to measure specific qualities (i.e. CPU utilization) that may indicate a problem. This fragmented approach is outdated in today's complex, dynamic virtual environments.

Meeting the Need for Fast, Accurate Resolution and Prevention of Application Performance Issues

SIOS iQ replaces the alert storms, inaccuracies, and manual effort associated with legacy tools with simple, precise, and clear recommendations for problem solving. By applying patented machine learning/deep learning analytics to a broad range of data from across IT silos, SIOS iQ learns the patterns of behavior observed across interrelated components over time. It correlates this anomalous behavior to application performance issues and changes in the infrastructure. SIOS iQ not only detects problems earlier with greater precision and clarity than traditional tools, but also identifies the root cause of issues, reveals unknown problems, and predicts future performance issues. IT can also use SIOS iQ to look at "what if" scenarios to understand the potential impact that a planned change will have on application performance before the change is actually implemented.

Finding the Root Cause of Performance Issues

"The exponential growth of modern IT infrastructures in both scale and complexity is pushing IT teams to their limits," said Jerry Melnick, president and CEO of SIOS Technology Corp. "SIOS iQ frees IT from the daily grind of reactive problem handling to proactively operate and innovate in order to add value to their core business operations. Deep learning technology in SIOS iQ analyzes tens of thousands of real-time metrics to accurately identify the root causes of performance issues and recommend specific steps to resolve them. Advanced predictive analytics in SIOS iQ forecasts future performance challenges so IT can avoid or prevent them before they occur."

SIOS iQ can be operated as a standalone tool to find and forecast infrastructure issues and their root cause or as a foundational platform of an enterprise analytics architecture that integrates with a wide variety of application performance monitoring tools.

New Features and Benefits

In addition to the flexible machine learning architecture, this update of SIOS iQ includes the following features:

• Software Developer Kit (SDK) for Broad Integration to include data from a wider range of sources including application and network monitoring tools and data aggregation fabrics such as Splunk. This SDK enables IT to query Splunk data more easily and to apply SIOS iQ machine learning-based analysis for more precise and comprehensive insights into application performance issues.
• Meta-analysis Provides Industry's Most Accurate Root Cause Identification. New deep learning technique identifies patterns of incidents related to application quality of service (QoS) reducing these to a small number of recurring infrastructure behaviors underlying the problem, revealing the root cause and providing precise recommendations for fixing them using a powerful new visualization technique.
• VM Packing and Placement: Recommends placement of workloads on VMware hosts to optimize VM density without the recurring thrashing and constant rebalancing caused by traditional tools.
• Enhanced Recommendations: Provides specific steps IT admins can take to solve complex performance issues and optimize efficiency.
• ROI Savings Analysis: Identifies wasted resources including rogue VMDKs, idle and oversized VMs, snapshot waste, unnecessary software licenses and wasted labor costs.
• Service Analytics: Allows IT admins to logically group and prioritize resources according to business importance. It correlates application service alerts with abnormal infrastructure behavior for fast, precise problem solving.