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SIOS Technology Announces New Version of SIOS iQ

SIOS Technology 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.

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.

“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.

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.

SIOS iQ is available immediately.

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SIOS Technology Announces New Version of SIOS iQ

SIOS Technology 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.

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.

“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.

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.

SIOS iQ is available immediately.

The Latest

While 87% of manufacturing leaders and technical specialists report that ROI from their AIOps initiatives has met or exceeded expectations, only 37% say they are fully prepared to operationalize AI at scale, according to The Future of IT Operations in the AI Era, a report from Riverbed ...

Many organizations rely on cloud-first architectures to aggregate, analyze, and act on their operational data ... However, not all environments are conducive to cloud-first architectures ... There are limitations to cloud-first architectures that render them ineffective in mission-critical situations where responsiveness, cost control, and data sovereignty are non-negotiable; these limitations include ...

For years, cybersecurity was built around a simple assumption: protect the physical network and trust everything inside it. That model made sense when employees worked in offices, applications lived in data centers, and devices rarely left the building. Today's reality is fluid: people work from everywhere, applications run across multiple clouds, and AI-driven agents are beginning to act on behalf of users. But while the old perimeter dissolved, a new one quietly emerged ...

For years, infrastructure teams have treated compute as a relatively stable input. Capacity was provisioned, costs were forecasted, and performance expectations were set based on the assumption that identical resources behaved identically. That mental model is starting to break down. AI infrastructure is no longer behaving like static cloud capacity. It is increasingly behaving like a market ...

Resilience can no longer be defined by how quickly an organization recovers from an incident or disruption. The effectiveness of any resilience strategy is dependent on its ability to anticipate change, operate under continuous stress, and adapt confidently amid uncertainty ...

Mobile users are less tolerant of app instability than ever before. According to a new report from Luciq, No Margin for Error: What Mobile Users Expect and What Mobile Leaders Must Deliver in 2026, even minor performance issues now result in immediate abandonment, lost purchases, and long-term brand impact ...

Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...

Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...

AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...

More data center leaders are reducing their reliance on utility grids by investing in onsite power for rapidly scaling data centers, according to the Data Center Power Report from Bloom Energy ...