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The Top 5 Features to Look for in VM Management

Industry insiders recommend the top features to look for in a solution to manage performance in the virtual environment.

1. Integration of Physical and Virtual Environments

“Look for a tool that integrates physical and virtual environments into a single pane of glass,” says Olivier Thierry, CMO of Zenoss. “You don’t want to create more silos.”

Thierry warns that it is very easy to establish a new silo of tools and operations staff to handle the virtual environment, but this only makes business service management more complex.

“You will always have a mixed environment of physical and virtual,” agrees Troy DuMoulin, ITIL Service Manager, AVP Product Strategy, Pink Elephant. “I can see the logic of having a single tool that allows you to manage both physical and virtual. You want one management interface that allows you to model and manage all different types of objects, regardless of where they are.”

2. End-to-End Visibility

“End-to-end visibility is a requirement,” says Javier Soltero, Chief Technology Officer for Management Products for SpringSource, a division of VMware. “You need the ability to see not just the hypervisor but through the guest operating system and whatever application components are running inside of that guest.”

3. Change Awareness

“Look for a tool that understands the dynamics of motion,” Thierry advises.

Javier Soltero defines this as “change awareness”, noting, “In a virtual environment, you have the ability to move workloads, and start and stop workloads as whole machines, basically by just going to vCenter and dragging things around, and starting and stopping them. You need to have a management tool that successfully operates within that environment.”

Soltero says the tool must honor the fact that when you VMotion from one hypervisor to the other, nothing happened from the perspective of the guest operating system in the application. On the other hand, from the hypervisor perspective, the tool must also recognize that you actually moved this workload from this vSphere host to another, and make sure that was successful and had no impact on the application running on top of it.

4. Built for the New Virtual Environment

“Many legacy tools just build virtualization management onto their products,” warns Thierry. “Unless the tool has a real-time model with dependency mapping configuration built into it, the tool will not be able to do it.”

“Look for a tool that has been purpose-built for this new virtual world,” he continues. “You can’t take a 1930s car and bolt on a brand new turbo charger. It was not designed for that.”

5. Cost Effectiveness

“Look for a management tool that is cost-effective,” Thierry concludes. “The reason for virtualization is to save money, so you do not want to go back and add a seven-figure systems management tool on top of that. The last thing you want to do is take a brand new cost-effective agile platform and dump a whole bunch of legacy, inappropriate, expensive, cumbersome, complex tooling on top. The cost equation must be maintained.”

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Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

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Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

The Top 5 Features to Look for in VM Management

Industry insiders recommend the top features to look for in a solution to manage performance in the virtual environment.

1. Integration of Physical and Virtual Environments

“Look for a tool that integrates physical and virtual environments into a single pane of glass,” says Olivier Thierry, CMO of Zenoss. “You don’t want to create more silos.”

Thierry warns that it is very easy to establish a new silo of tools and operations staff to handle the virtual environment, but this only makes business service management more complex.

“You will always have a mixed environment of physical and virtual,” agrees Troy DuMoulin, ITIL Service Manager, AVP Product Strategy, Pink Elephant. “I can see the logic of having a single tool that allows you to manage both physical and virtual. You want one management interface that allows you to model and manage all different types of objects, regardless of where they are.”

2. End-to-End Visibility

“End-to-end visibility is a requirement,” says Javier Soltero, Chief Technology Officer for Management Products for SpringSource, a division of VMware. “You need the ability to see not just the hypervisor but through the guest operating system and whatever application components are running inside of that guest.”

3. Change Awareness

“Look for a tool that understands the dynamics of motion,” Thierry advises.

Javier Soltero defines this as “change awareness”, noting, “In a virtual environment, you have the ability to move workloads, and start and stop workloads as whole machines, basically by just going to vCenter and dragging things around, and starting and stopping them. You need to have a management tool that successfully operates within that environment.”

Soltero says the tool must honor the fact that when you VMotion from one hypervisor to the other, nothing happened from the perspective of the guest operating system in the application. On the other hand, from the hypervisor perspective, the tool must also recognize that you actually moved this workload from this vSphere host to another, and make sure that was successful and had no impact on the application running on top of it.

4. Built for the New Virtual Environment

“Many legacy tools just build virtualization management onto their products,” warns Thierry. “Unless the tool has a real-time model with dependency mapping configuration built into it, the tool will not be able to do it.”

“Look for a tool that has been purpose-built for this new virtual world,” he continues. “You can’t take a 1930s car and bolt on a brand new turbo charger. It was not designed for that.”

5. Cost Effectiveness

“Look for a management tool that is cost-effective,” Thierry concludes. “The reason for virtualization is to save money, so you do not want to go back and add a seven-figure systems management tool on top of that. The last thing you want to do is take a brand new cost-effective agile platform and dump a whole bunch of legacy, inappropriate, expensive, cumbersome, complex tooling on top. The cost equation must be maintained.”

Hot Topics

The Latest

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...