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SIOS Technology Announces New Release of SIOS iQ

SIOS Technology Corp. announced the latest release of SIOS iQ machine learning analytics software, which has new features that deliver unparalleled accuracy and precision in capacity utilization and performance root cause analysis for VMware environments.

It also includes dashboard enhancements for improved usability and a graphical topological impact view enabling faster identification and resolution of issues.

“Legacy monitoring tools provide data about individual objects, such as CPU or capacity utilization). When a performance problem arises they leave IT staff to compare data points to make educated guesses about both the root cause and potential solution,” said Jerry Melnick, SIOS President and CEO. “SIOS iQ not only eliminates this guesswork by precisely identifying the cause, but it also recommends specific steps to resolve it.”

“Virtualization promises a variety of benefits, including cost savings, improved resource utilization, and efficiency, but the complex, dynamic nature of virtual environments can sometime obscure conflicts and wastage,” said Nik Rouda, Senior Analyst, ESG. “SIOS iQ leverages the power of machine learning analytics to help companies by transforming enormous volumes of data about virtual infrastructures into easily understood, actionable recommendations — a winning approach for enterprises.”

The version 3.5 release is the fifth product update SIOS has released since launching the SIOS iQ product in July 2015.

Designed to be a powerful platform for IT operations information and issue resolution, SIOS iQ applies an advanced data analytics/Big Data approach to a broad range of data sets, including application and infrastructure data from third party tools and frameworks, to recognize abnormal patterns of behavior and identify root causes of performance issues. It provides information organized according to four key dimensions: performance, efficiency, reliability, and capacity utilization. The latest innovations from SIOS deliver industry leading simplicity and accuracy in identifying and resolving root causes of performance issues and predicting capacity needs.

SIOS iQ features are released on an ongoing basis. Version 3.5 includes the following new features:

- Capacity Forecasting Analysis – SIOS iQ understands capacity utilization pattern to forecast how many days remain before data store(s) run out of free space. This feature optimizes infrastructure without risking costly emergencies. It can be used with the SIOS iQ Snapshot Waste analysis feature to optimize storage and maintain a predictable budget.

- Enhanced Root Cause Analysis – This feature adds symptom analytics and graphically describes the topology of the impacted objects visually showing the user the infrastructure issue. In one click, it provides a deep understanding of issues by employing advanced topological behavior analysis to provide root cause of performance issues without the need to manually parse detailed data logs or compile and compare charts.

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SIOS Technology Announces New Release of SIOS iQ

SIOS Technology Corp. announced the latest release of SIOS iQ machine learning analytics software, which has new features that deliver unparalleled accuracy and precision in capacity utilization and performance root cause analysis for VMware environments.

It also includes dashboard enhancements for improved usability and a graphical topological impact view enabling faster identification and resolution of issues.

“Legacy monitoring tools provide data about individual objects, such as CPU or capacity utilization). When a performance problem arises they leave IT staff to compare data points to make educated guesses about both the root cause and potential solution,” said Jerry Melnick, SIOS President and CEO. “SIOS iQ not only eliminates this guesswork by precisely identifying the cause, but it also recommends specific steps to resolve it.”

“Virtualization promises a variety of benefits, including cost savings, improved resource utilization, and efficiency, but the complex, dynamic nature of virtual environments can sometime obscure conflicts and wastage,” said Nik Rouda, Senior Analyst, ESG. “SIOS iQ leverages the power of machine learning analytics to help companies by transforming enormous volumes of data about virtual infrastructures into easily understood, actionable recommendations — a winning approach for enterprises.”

The version 3.5 release is the fifth product update SIOS has released since launching the SIOS iQ product in July 2015.

Designed to be a powerful platform for IT operations information and issue resolution, SIOS iQ applies an advanced data analytics/Big Data approach to a broad range of data sets, including application and infrastructure data from third party tools and frameworks, to recognize abnormal patterns of behavior and identify root causes of performance issues. It provides information organized according to four key dimensions: performance, efficiency, reliability, and capacity utilization. The latest innovations from SIOS deliver industry leading simplicity and accuracy in identifying and resolving root causes of performance issues and predicting capacity needs.

SIOS iQ features are released on an ongoing basis. Version 3.5 includes the following new features:

- Capacity Forecasting Analysis – SIOS iQ understands capacity utilization pattern to forecast how many days remain before data store(s) run out of free space. This feature optimizes infrastructure without risking costly emergencies. It can be used with the SIOS iQ Snapshot Waste analysis feature to optimize storage and maintain a predictable budget.

- Enhanced Root Cause Analysis – This feature adds symptom analytics and graphically describes the topology of the impacted objects visually showing the user the infrastructure issue. In one click, it provides a deep understanding of issues by employing advanced topological behavior analysis to provide root cause of performance issues without the need to manually parse detailed data logs or compile and compare charts.

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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...