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BSM to the Rescue

Survey Says: Monitoring and Managing the Cloud is Top Concern

In September, Zenoss released the 2010 Virtualization and Cloud Computing Survey, which took the temperature of over 200 IT professionals about their reasons for using virtualization and the cloud. Some of our results were about as surprising as rain in Seattle: 40.7% of survey respondents said they prefer to deploy servers virtually, 79.3% of them said they are using VMware and the number one goal with regards to using virtual infrastructure was to save money. Many of the results were more intriguing, but the conclusion that most piqued our interest here at Zenoss was that the number two concern about cloud computing, after security, was management/monitoring.

As cloud computing becomes more popular it’s also becoming more complex. In Pete Goldin’s article in BSMdigest last May (“Virtualization Changes Everything”), Olivier Thierry of Zenoss talked a little bit about the tricky new layer that virtualization has created. Virtualization makes many aspects of business easier by automating processes, but it also requires an entirely different set of tools from those used to manage a traditional, physical environment. Most legacy monitoring and management solutions are ill equipped to handle the cloud because they’re static, fragmented and single-tenant. In contrast, newer tools need to be real-time, unified and multi-tenant in order to offer visibility and control of a dynamic cloud infrastructure. All too often these old tools and new tools don’t play nicely together and the task of integrating them has created a need for specialists. As a result, virtualization management today can be a dreadful silo and as it turns out, nobody likes that: 70.7% of the people we surveyed prefer tools that manage all infrastructure rather than point solutions that are specific to virtualization.

There are many ways in which business service management (BSM) can help break up that silo and navigate the often challenging task of deploying a private or public cloud environment. If a business application fails, a common reaction is to reprovision (a.k.a. pile on more resources). But the only way to solve the problem is to find the problem first: figure out why something didn’t perform by using a tool that can keep up with a continuously changing configuration. Network monitoring and systems management providers like Zenoss and others can offer answers to critical questions about servers and dependencies. With the right set of management tools, processes and methods, businesses can be confident that their IT infrastructures are working efficiently.

But what if your organization isn’t ready to virtualize completely? After all, 73.3% of our survey respondents hadn’t made a decision on their virtualization management solution and only 29.3% said they wanted to use virtualization everywhere. Though I can understand their concerns, virtualization is here to stay and cloud computing’s numerous advantages are too compelling to ignore completely. I’d advise those who are hesitant not to wait, but to take things one at a time. Start by virtualizing part of your infrastructure. Integrate the management of your virtualized environment with your physical datacenter, and then with the proper visualization and management tool in place so as to mitigate the risks, continue the journey to a more virtualized IT world.

About Bill Karpovich

Bill Karpovich, CEO and Co-Founder of Zenoss, conceived the company's disruptive business strategy and has successfully guided the company from start-up to a category leader. As an IT management and cloud computing visionary, Bill has been featured on the cover of InformationWeek Magazine and is frequently consulted by the media and industry analysts for his insights on IT management and the broader open source software market.

Hot Topics

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In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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.

BSM to the Rescue

Survey Says: Monitoring and Managing the Cloud is Top Concern

In September, Zenoss released the 2010 Virtualization and Cloud Computing Survey, which took the temperature of over 200 IT professionals about their reasons for using virtualization and the cloud. Some of our results were about as surprising as rain in Seattle: 40.7% of survey respondents said they prefer to deploy servers virtually, 79.3% of them said they are using VMware and the number one goal with regards to using virtual infrastructure was to save money. Many of the results were more intriguing, but the conclusion that most piqued our interest here at Zenoss was that the number two concern about cloud computing, after security, was management/monitoring.

As cloud computing becomes more popular it’s also becoming more complex. In Pete Goldin’s article in BSMdigest last May (“Virtualization Changes Everything”), Olivier Thierry of Zenoss talked a little bit about the tricky new layer that virtualization has created. Virtualization makes many aspects of business easier by automating processes, but it also requires an entirely different set of tools from those used to manage a traditional, physical environment. Most legacy monitoring and management solutions are ill equipped to handle the cloud because they’re static, fragmented and single-tenant. In contrast, newer tools need to be real-time, unified and multi-tenant in order to offer visibility and control of a dynamic cloud infrastructure. All too often these old tools and new tools don’t play nicely together and the task of integrating them has created a need for specialists. As a result, virtualization management today can be a dreadful silo and as it turns out, nobody likes that: 70.7% of the people we surveyed prefer tools that manage all infrastructure rather than point solutions that are specific to virtualization.

There are many ways in which business service management (BSM) can help break up that silo and navigate the often challenging task of deploying a private or public cloud environment. If a business application fails, a common reaction is to reprovision (a.k.a. pile on more resources). But the only way to solve the problem is to find the problem first: figure out why something didn’t perform by using a tool that can keep up with a continuously changing configuration. Network monitoring and systems management providers like Zenoss and others can offer answers to critical questions about servers and dependencies. With the right set of management tools, processes and methods, businesses can be confident that their IT infrastructures are working efficiently.

But what if your organization isn’t ready to virtualize completely? After all, 73.3% of our survey respondents hadn’t made a decision on their virtualization management solution and only 29.3% said they wanted to use virtualization everywhere. Though I can understand their concerns, virtualization is here to stay and cloud computing’s numerous advantages are too compelling to ignore completely. I’d advise those who are hesitant not to wait, but to take things one at a time. Start by virtualizing part of your infrastructure. Integrate the management of your virtualized environment with your physical datacenter, and then with the proper visualization and management tool in place so as to mitigate the risks, continue the journey to a more virtualized IT world.

About Bill Karpovich

Bill Karpovich, CEO and Co-Founder of Zenoss, conceived the company's disruptive business strategy and has successfully guided the company from start-up to a category leader. As an IT management and cloud computing visionary, Bill has been featured on the cover of InformationWeek Magazine and is frequently consulted by the media and industry analysts for his insights on IT management and the broader open source software market.

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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.