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eG Innovations Awarded Two Patents for Virtualization Performance Management

eG Innovations has been issued two patents for its virtualization monitoring and root-cause diagnosis technologies.

US Patent 8,208,381 deals with automatic, virtualization-aware root-cause diagnosis of business service performance issues across network, system, application and virtualization tiers.

US Patent 8,209,684 deals with eG Innovations In-N-Out monitoring technology for virtual infrastructures that provides correlated outside and inside view of virtual machines.

“eG Innovations has a long tradition of industry ‘firsts’ because of our strong commitment to innovation and helping customers overcome the challenges in performance management of dynamic IT environments,” said Srinivas Ramanathan, founder and CEO, eG Innovations.

When users call and complain about slow applications, it is often difficult and very time consuming to pinpoint the root cause of the problem with accuracy. Is it the network, Citrix, VMware, is it the Java application, database server, or storage, is it the network or is there a problem in the cloud? Is it user behavior or application problems?

eG Enterprise, the flagship product suite from eG Innovations, automates and dramatically accelerates the discovery, diagnosis and resolution of performance issues – across virtual, physical and cloud environments. This way IT staff can resolve performance issues with accuracy in minutes rather than days or weeks, and highly skilled IT staff can be more productive rather than fighting fires all day.

Virtualized IT environments are highly dynamic and heterogeneous, making manual performance diagnosis with legacy tools virtually impossible. eG’s unique, patented In-N-Out technology delivers both depth and breadth of insight into virtual application performance, a critical capability for rapid and accurate diagnosis of performance problems. It provides both an outside view of a VM indicating the hypervisor’s physical resources used by a VM, and an inside view of the VM indicating which application(s) and user(s) of the VM are causing the resource usage. This capability is particularly useful in virtual desktop infrastructures, where it is not possible to deploy monitoring agents on each and every virtual desktop, yet it is critical to understand what is happening inside each virtual desktop.

Related Links:

www.eginnovations.com

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

eG Innovations Awarded Two Patents for Virtualization Performance Management

eG Innovations has been issued two patents for its virtualization monitoring and root-cause diagnosis technologies.

US Patent 8,208,381 deals with automatic, virtualization-aware root-cause diagnosis of business service performance issues across network, system, application and virtualization tiers.

US Patent 8,209,684 deals with eG Innovations In-N-Out monitoring technology for virtual infrastructures that provides correlated outside and inside view of virtual machines.

“eG Innovations has a long tradition of industry ‘firsts’ because of our strong commitment to innovation and helping customers overcome the challenges in performance management of dynamic IT environments,” said Srinivas Ramanathan, founder and CEO, eG Innovations.

When users call and complain about slow applications, it is often difficult and very time consuming to pinpoint the root cause of the problem with accuracy. Is it the network, Citrix, VMware, is it the Java application, database server, or storage, is it the network or is there a problem in the cloud? Is it user behavior or application problems?

eG Enterprise, the flagship product suite from eG Innovations, automates and dramatically accelerates the discovery, diagnosis and resolution of performance issues – across virtual, physical and cloud environments. This way IT staff can resolve performance issues with accuracy in minutes rather than days or weeks, and highly skilled IT staff can be more productive rather than fighting fires all day.

Virtualized IT environments are highly dynamic and heterogeneous, making manual performance diagnosis with legacy tools virtually impossible. eG’s unique, patented In-N-Out technology delivers both depth and breadth of insight into virtual application performance, a critical capability for rapid and accurate diagnosis of performance problems. It provides both an outside view of a VM indicating the hypervisor’s physical resources used by a VM, and an inside view of the VM indicating which application(s) and user(s) of the VM are causing the resource usage. This capability is particularly useful in virtual desktop infrastructures, where it is not possible to deploy monitoring agents on each and every virtual desktop, yet it is critical to understand what is happening inside each virtual desktop.

Related Links:

www.eginnovations.com

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...