Skip to main content

CloudPhysics Introduces Global and Daily Insights

CloudPhysics announced significant enhancements to its SaaS-based solution, including new Global Insights that make it possible for VMware users to continuously benchmark their virtual infrastructure against global metrics.

In combination with new interactive Daily Insights, which dynamically aggregate and expose operational hazards from across the datacenter, Global Insights enable CloudPhysics users to instantly identify areas for improvement in their own environments as well as specific actions for achieving better datacenter health, performance and efficiency.

CloudPhysics is also previewing its “workload shapes” technology, an industry-first that provides VMware administrators with a visual shorthand for quickly recognizing storage performance anomalies, and deep visibility for accelerating resolution.

“Today’s announcement delivers further on CloudPhysics’ commitment to use Big Data to help IT teams make smarter operational decisions for better datacenters,” said John Blumenthal, CloudPhysics VP of Product Management. “We continue to formulate new ways to put data to work for our customers, yielding relevant insights at the right time, in the right context. From broad aggregation of operational metrics gathered across thousands of datacenters, to highly granular views into individual workload shapes, our data-driven insights give IT teams more power than ever before to understand, troubleshoot, and optimize their virtualized datacenters.”

CloudPhysics’ new features and technology preview will be unveiled at booth #640 at VMworld 2014, where the company is also sponsoring a contest to win Google Glass. In this show of skill, attendees can demonstrate their vSphere expertise by guessing the percentage change of key datacenter metrics over the past two years according to CloudPhysics’ global data set. More contest information can be found here.

CloudPhysics’ cloud-based platform collects and analyzes a daily stream of configuration, performance, failure and event data from a global user base, with a total of 50+ trillion samples collected to date. Combining this Big Data with unique patent-pending datacenter simulation and resource management techniques, CloudPhysics identifies global trends and patterns of behavior. This Collective Intelligence is passed on to customers through the algorithms that drive the new features and capabilities announced today:

- Global Benchmarks and Daily Insights: A new interactive console provides Global and Daily Insights, both of which are continually refreshed. Global Insights compare a user’s key datacenter metrics against those from CloudPhysics’ unique global data set, providing useful benchmarks for evaluating relative performance, health and efficiency. Daily Insights aggregate alerts and recommendations generated by CloudPhysics across the datacenter and, with new hyperlinking capabilities, provide the ability to navigate and drill down contextually to gain deeper visibility, accelerate resolution and improve overall datacenter metrics.

- Enhanced Performance Troubleshooting: Building on the recently released Datastore Contention analytic, which provides insights into disk I/O contention at the datastore level, CloudPhysics’ new VM Disk I/O Contention analytic focuses on VM-level performance. An interactive timeline visually correlates patterns among datastores/VMs, and dramatically simplifies the exploration of hotspots. The algorithm used to detect contention has been tuned and validated using CloudPhysics’ Collective Intelligence.

- New Smart Alerts: “Guest Partition in VM Running Out of Space” and “Unused VMs” further expand CloudPhysics’ set of SmartAlerts, first introduced in June. CloudPhysics evaluates all objects in the virtual datacenter against certain criteria (e.g., latency, duration, outstanding IOs, IOPS, etc.) and triggers Smart Alerts based on thresholds derived dynamically from patterns and trends observed across our global dataset. Users foresee when conditions are degrading and receive specific recommendations for preemptive measures.

- Workload Shapes (Technology Preview): CloudPhysics analyzes and characterizes storage workload spatial locality (sequentiality vs randomness), dominant I/O block sizes, and the complete latency profile, then visualizes these into “workload shapes.” Administrators quickly learn which shapes are “normal” for their environment and identify outliers that indicate performance troublespots, accelerating time to resolution. This deep visibility comes from unique underlying technology, in which every I/O is analyzed, ensuring anomalies don’t get lost in the averages, which is what happens with most other tools.

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 ...

CloudPhysics Introduces Global and Daily Insights

CloudPhysics announced significant enhancements to its SaaS-based solution, including new Global Insights that make it possible for VMware users to continuously benchmark their virtual infrastructure against global metrics.

In combination with new interactive Daily Insights, which dynamically aggregate and expose operational hazards from across the datacenter, Global Insights enable CloudPhysics users to instantly identify areas for improvement in their own environments as well as specific actions for achieving better datacenter health, performance and efficiency.

CloudPhysics is also previewing its “workload shapes” technology, an industry-first that provides VMware administrators with a visual shorthand for quickly recognizing storage performance anomalies, and deep visibility for accelerating resolution.

“Today’s announcement delivers further on CloudPhysics’ commitment to use Big Data to help IT teams make smarter operational decisions for better datacenters,” said John Blumenthal, CloudPhysics VP of Product Management. “We continue to formulate new ways to put data to work for our customers, yielding relevant insights at the right time, in the right context. From broad aggregation of operational metrics gathered across thousands of datacenters, to highly granular views into individual workload shapes, our data-driven insights give IT teams more power than ever before to understand, troubleshoot, and optimize their virtualized datacenters.”

CloudPhysics’ new features and technology preview will be unveiled at booth #640 at VMworld 2014, where the company is also sponsoring a contest to win Google Glass. In this show of skill, attendees can demonstrate their vSphere expertise by guessing the percentage change of key datacenter metrics over the past two years according to CloudPhysics’ global data set. More contest information can be found here.

CloudPhysics’ cloud-based platform collects and analyzes a daily stream of configuration, performance, failure and event data from a global user base, with a total of 50+ trillion samples collected to date. Combining this Big Data with unique patent-pending datacenter simulation and resource management techniques, CloudPhysics identifies global trends and patterns of behavior. This Collective Intelligence is passed on to customers through the algorithms that drive the new features and capabilities announced today:

- Global Benchmarks and Daily Insights: A new interactive console provides Global and Daily Insights, both of which are continually refreshed. Global Insights compare a user’s key datacenter metrics against those from CloudPhysics’ unique global data set, providing useful benchmarks for evaluating relative performance, health and efficiency. Daily Insights aggregate alerts and recommendations generated by CloudPhysics across the datacenter and, with new hyperlinking capabilities, provide the ability to navigate and drill down contextually to gain deeper visibility, accelerate resolution and improve overall datacenter metrics.

- Enhanced Performance Troubleshooting: Building on the recently released Datastore Contention analytic, which provides insights into disk I/O contention at the datastore level, CloudPhysics’ new VM Disk I/O Contention analytic focuses on VM-level performance. An interactive timeline visually correlates patterns among datastores/VMs, and dramatically simplifies the exploration of hotspots. The algorithm used to detect contention has been tuned and validated using CloudPhysics’ Collective Intelligence.

- New Smart Alerts: “Guest Partition in VM Running Out of Space” and “Unused VMs” further expand CloudPhysics’ set of SmartAlerts, first introduced in June. CloudPhysics evaluates all objects in the virtual datacenter against certain criteria (e.g., latency, duration, outstanding IOs, IOPS, etc.) and triggers Smart Alerts based on thresholds derived dynamically from patterns and trends observed across our global dataset. Users foresee when conditions are degrading and receive specific recommendations for preemptive measures.

- Workload Shapes (Technology Preview): CloudPhysics analyzes and characterizes storage workload spatial locality (sequentiality vs randomness), dominant I/O block sizes, and the complete latency profile, then visualizes these into “workload shapes.” Administrators quickly learn which shapes are “normal” for their environment and identify outliers that indicate performance troublespots, accelerating time to resolution. This deep visibility comes from unique underlying technology, in which every I/O is analyzed, ensuring anomalies don’t get lost in the averages, which is what happens with most other tools.

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 ...