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Widespread Downtime Found in 99 Percent of Cloud Environments

Downtime and security risks were present in each cloud environment tested, according to 2016 Private Cloud Resiliency Benchmarks, a report from Continuity Software.

The study also found that security and performance risks were found in 99 percent and 97 percent of the environments respectively, with 82 percent of the companies facing data loss risks.

Some of the top risks identified across the private cloud environments include:

■ Configuration drifts between cluster nodes that prevent failover. Examples for such discrepancies range from the most trivial – e.g., a file that is not accessible by all hosts in the cluster – to more complex ones – such as incorrect settings of affinity rules.

■ Virtual networking configuration errors leading to virtual machine isolation and downtime. Examples include incorrect Virtual Machine Port Group configurations and resources misalignment between ESXi cluster hosts leading to a single point of failure.

■ Incorrect storage settings leading to corrupt backups and data store loss. Such risks range from invalid CBT configuration to inconsistent LUN numbering and incorrect UUID settings.

What do these private cloud environments look like?

■ 48 percent of the organizations included in the study run their virtual machines on Windows compared to 7 percent of the organizations that run on Linux. 46 percent of the organizations use a mix of operating systems.

■ Close to three quarters (73 percent) of the organizations use EMC data storage systems. Other storage systems used include NetApp (38 percent), IBM (26 percent), HP (24 percent) and Hitachi (18 percent).

■ 27 percent of the organizations use replication for automated offsite data protection.

■ 12 percent of the organizations utilize active-active failover for continuous availability.

■ Almost all of the organizations (96 percent) use more than one physical path to transfer data between the host and the external storage device.

With a growing level of the complexity, increasing interdependence among infrastructure components, and an escalating pace of change, keeping cloud infrastructure free of risky misconfiguration is becoming a challenge that most organizations fail to meet.

"Sooner or later, every system fails," said Gil Hecht, CEO of Continuity Software. "And when a popular service goes down, it doesn't take long for customers to notice."

Each year enterprises continue to encounter downtime, which currently costs an estimated $740,000 per outage according to Ponemon's most recent report.

"The good news is that most risks lurking in the cloud infrastructure can be identified and corrected before they turn into a service disruption," explained Hecht. "This requires a specialized set of processes and tools, but above all a mindset and strategy focused on early detection and the remediation of risks."

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Widespread Downtime Found in 99 Percent of Cloud Environments

Downtime and security risks were present in each cloud environment tested, according to 2016 Private Cloud Resiliency Benchmarks, a report from Continuity Software.

The study also found that security and performance risks were found in 99 percent and 97 percent of the environments respectively, with 82 percent of the companies facing data loss risks.

Some of the top risks identified across the private cloud environments include:

■ Configuration drifts between cluster nodes that prevent failover. Examples for such discrepancies range from the most trivial – e.g., a file that is not accessible by all hosts in the cluster – to more complex ones – such as incorrect settings of affinity rules.

■ Virtual networking configuration errors leading to virtual machine isolation and downtime. Examples include incorrect Virtual Machine Port Group configurations and resources misalignment between ESXi cluster hosts leading to a single point of failure.

■ Incorrect storage settings leading to corrupt backups and data store loss. Such risks range from invalid CBT configuration to inconsistent LUN numbering and incorrect UUID settings.

What do these private cloud environments look like?

■ 48 percent of the organizations included in the study run their virtual machines on Windows compared to 7 percent of the organizations that run on Linux. 46 percent of the organizations use a mix of operating systems.

■ Close to three quarters (73 percent) of the organizations use EMC data storage systems. Other storage systems used include NetApp (38 percent), IBM (26 percent), HP (24 percent) and Hitachi (18 percent).

■ 27 percent of the organizations use replication for automated offsite data protection.

■ 12 percent of the organizations utilize active-active failover for continuous availability.

■ Almost all of the organizations (96 percent) use more than one physical path to transfer data between the host and the external storage device.

With a growing level of the complexity, increasing interdependence among infrastructure components, and an escalating pace of change, keeping cloud infrastructure free of risky misconfiguration is becoming a challenge that most organizations fail to meet.

"Sooner or later, every system fails," said Gil Hecht, CEO of Continuity Software. "And when a popular service goes down, it doesn't take long for customers to notice."

Each year enterprises continue to encounter downtime, which currently costs an estimated $740,000 per outage according to Ponemon's most recent report.

"The good news is that most risks lurking in the cloud infrastructure can be identified and corrected before they turn into a service disruption," explained Hecht. "This requires a specialized set of processes and tools, but above all a mindset and strategy focused on early detection and the remediation of risks."

Hot Topics

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 ...