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Four Practical Steps to Private Cloud Computing

If it is your job to translate overhyped demands to take your business ‘To The Cloud!’ you know there is not enough reality in cloud computing. You cannot start from scratch, nor can you simply deploy dynamic virtualization and call it done. You must accommodate legacy investments, architectural spaghetti, ‘technical debt’, manual processes and more. So where do you start?

In our recent book, Visible Ops - Private Cloud: From Virtualization to Private Cloud in 4 Practical Steps, my co-authors (Kurt Milne, Jeanne Morain) and I spoke with dozens of IT leaders about their experiences building their own ‘private clouds’. By documenting the successes and failures common to the best performers, we came up with a realistic stepwise process that builds on legacy investments, capitalizes on existing skills, and incorporates necessary processes, to deliver the benefits of cloud computing.

Phase 1: Cut through the cloud clutter

The first step entails planning and communicating objectives, managing initial proof of concept efforts, and developing competency roadmaps.

Successful cloud implementations result from executing a business strategy, not rolling out new IT projects. You need to cut through the hype by establishing a service portfolio view of infrastructure and applications, measuring current service performance and cost, setting goals for service improvement, and establishing some initial success, before you start transforming virtual infrastructure into private cloud.

Understanding application performance and response times, service fulfillment cycles, service level metrics, key competencies, operating and capital costs, etc. allows you to plan achievable improvements. This in turn helps to cut through the hype in order to show your business what they should realistically expect from your private cloud strategy.

Phase 2: Design services, not systems

With a plan in place, start to design business optimized cloud services, enable one-touch service ordering, and implement a repeatable approach for build and deploy.
Business services must be standardized, cataloged, and automated to establish repeatable user-driven onramps to deploying resources. This requires a new approach to Business Service Management to avoid an ever-expanding complex catalog of bespoke ‘services’ that are never deployed the same way twice.

This is a critical difference between building virtualized applications and delivering cloud services. IT-centric approaches that elevate administrative complexity and control will not work in dynamic cloud environments. Some essential aspects of legacy BSM frameworks remain important, but cloud computing will kill complex controls in favor of simplified enablement that puts business users in charge.

Phase 3: Orchestrate and optimize resources

With service design complete, you should update monitoring and alerting, codify policy-based event responses, and automate resource changes and workload moves.
Technologies like application performance management, resource optimization, and process automation are immensely important in a private cloud environment. An effective private cloud relies on technologies that monitor real-time performance of end-to-end business services, detect variations from defined performance models, diagnose the true root cause of problems, match performance requirements to available resource pool capacity, and automatically adjust and optimize resource allocation to match.

This is much more than just response time measurement and live migration. Effective private clouds optimize complete business services, not just virtual machines. Live migration is important, but not sufficient, to deliver a successful private cloud.

Phase 4: Align and accelerate business results

With the heavy technology lifting done, complete the transition to a resource rental model by reshaping consumption behavior and streamlining response to business needs.

This entails moving targeted workloads to your private cloud to leverage its benefits, understanding and communicating the service cost, quality, and agility measures of each cloud environment, and actively reshaping demand for IT resources using a rental model.

This change in business behavior enables private cloud to be successful in ways automated virtualization cannot. Virtualization is an IT-centric technology that does not require business users to change their behaviors, as IT is still in charge. With cloud computing, business users are in charge, so they must ‘learn’ some of the discipline needed to maintain acceptable cost, security, risk, performance, etc.

Summary

This is of course a simplified version of the practical four-step process from virtualization to cloud. Clearly developing and delivering your own private cloud is not even this simple. However, with a concise, practical, and realistic approach born of the real-world successes and failures of those who have already done it, as documented in Visible Ops - Private Cloud: From Virtualization to Private Cloud in 4 Practical Steps, you can achieve phenomenal results, drive IT efficiency, and deliver significant business benefits with your own private cloud.

About Andi Mann

Andi Mann is Vice President of Strategic Solutions at CA Technologies. With over 20 years’ experience across four continents, Andi has deep expertise of enterprise software on cloud, mainframe, midrange, server and desktop systems. Andi has worked within IT departments for governments and corporations, from small businesses to global multi-nationals; with several large enterprise software vendors; and as a leading industry analyst advising enterprises, governments, and IT vendors – from startups to the worlds’ largest companies. He has been widely published including in the New York Times, USA Today, CIO, ComputerWorld, InformationWeek, TechTarget, and more. He has presented around the world on virtualization, cloud, automation, and IT management, at events such as Gartner ITxpo, VMworld, CA World, Interop, Cloud Computing Expo, SAPPHIRE, Citrix Synergy, Cloud Slam, and others. Andi is a co-author of the popular handbook, Visible Ops – Private Cloud; he blogs at Andi Mann – Übergeek, and tweets as @AndiMann.

Related Links:

12 Things You Need to Know About Application Performance Management in the Cloud

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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 ...

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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 ...

Four Practical Steps to Private Cloud Computing

If it is your job to translate overhyped demands to take your business ‘To The Cloud!’ you know there is not enough reality in cloud computing. You cannot start from scratch, nor can you simply deploy dynamic virtualization and call it done. You must accommodate legacy investments, architectural spaghetti, ‘technical debt’, manual processes and more. So where do you start?

In our recent book, Visible Ops - Private Cloud: From Virtualization to Private Cloud in 4 Practical Steps, my co-authors (Kurt Milne, Jeanne Morain) and I spoke with dozens of IT leaders about their experiences building their own ‘private clouds’. By documenting the successes and failures common to the best performers, we came up with a realistic stepwise process that builds on legacy investments, capitalizes on existing skills, and incorporates necessary processes, to deliver the benefits of cloud computing.

Phase 1: Cut through the cloud clutter

The first step entails planning and communicating objectives, managing initial proof of concept efforts, and developing competency roadmaps.

Successful cloud implementations result from executing a business strategy, not rolling out new IT projects. You need to cut through the hype by establishing a service portfolio view of infrastructure and applications, measuring current service performance and cost, setting goals for service improvement, and establishing some initial success, before you start transforming virtual infrastructure into private cloud.

Understanding application performance and response times, service fulfillment cycles, service level metrics, key competencies, operating and capital costs, etc. allows you to plan achievable improvements. This in turn helps to cut through the hype in order to show your business what they should realistically expect from your private cloud strategy.

Phase 2: Design services, not systems

With a plan in place, start to design business optimized cloud services, enable one-touch service ordering, and implement a repeatable approach for build and deploy.
Business services must be standardized, cataloged, and automated to establish repeatable user-driven onramps to deploying resources. This requires a new approach to Business Service Management to avoid an ever-expanding complex catalog of bespoke ‘services’ that are never deployed the same way twice.

This is a critical difference between building virtualized applications and delivering cloud services. IT-centric approaches that elevate administrative complexity and control will not work in dynamic cloud environments. Some essential aspects of legacy BSM frameworks remain important, but cloud computing will kill complex controls in favor of simplified enablement that puts business users in charge.

Phase 3: Orchestrate and optimize resources

With service design complete, you should update monitoring and alerting, codify policy-based event responses, and automate resource changes and workload moves.
Technologies like application performance management, resource optimization, and process automation are immensely important in a private cloud environment. An effective private cloud relies on technologies that monitor real-time performance of end-to-end business services, detect variations from defined performance models, diagnose the true root cause of problems, match performance requirements to available resource pool capacity, and automatically adjust and optimize resource allocation to match.

This is much more than just response time measurement and live migration. Effective private clouds optimize complete business services, not just virtual machines. Live migration is important, but not sufficient, to deliver a successful private cloud.

Phase 4: Align and accelerate business results

With the heavy technology lifting done, complete the transition to a resource rental model by reshaping consumption behavior and streamlining response to business needs.

This entails moving targeted workloads to your private cloud to leverage its benefits, understanding and communicating the service cost, quality, and agility measures of each cloud environment, and actively reshaping demand for IT resources using a rental model.

This change in business behavior enables private cloud to be successful in ways automated virtualization cannot. Virtualization is an IT-centric technology that does not require business users to change their behaviors, as IT is still in charge. With cloud computing, business users are in charge, so they must ‘learn’ some of the discipline needed to maintain acceptable cost, security, risk, performance, etc.

Summary

This is of course a simplified version of the practical four-step process from virtualization to cloud. Clearly developing and delivering your own private cloud is not even this simple. However, with a concise, practical, and realistic approach born of the real-world successes and failures of those who have already done it, as documented in Visible Ops - Private Cloud: From Virtualization to Private Cloud in 4 Practical Steps, you can achieve phenomenal results, drive IT efficiency, and deliver significant business benefits with your own private cloud.

About Andi Mann

Andi Mann is Vice President of Strategic Solutions at CA Technologies. With over 20 years’ experience across four continents, Andi has deep expertise of enterprise software on cloud, mainframe, midrange, server and desktop systems. Andi has worked within IT departments for governments and corporations, from small businesses to global multi-nationals; with several large enterprise software vendors; and as a leading industry analyst advising enterprises, governments, and IT vendors – from startups to the worlds’ largest companies. He has been widely published including in the New York Times, USA Today, CIO, ComputerWorld, InformationWeek, TechTarget, and more. He has presented around the world on virtualization, cloud, automation, and IT management, at events such as Gartner ITxpo, VMworld, CA World, Interop, Cloud Computing Expo, SAPPHIRE, Citrix Synergy, Cloud Slam, and others. Andi is a co-author of the popular handbook, Visible Ops – Private Cloud; he blogs at Andi Mann – Übergeek, and tweets as @AndiMann.

Related Links:

12 Things You Need to Know About Application Performance Management in the Cloud

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 ...