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Ensuring Performance and Availability in a Virtual Environment

Jerry Melnick

Optimizing and ensuring performance and availability in a virtual (VMware) environment is far more challenging than the orderly and disciplined environment of dedicated physical servers. In virtual environments VMs, applications, storage, network, and other IT services share IT resources and operate in direct relationship to one another. A new or moved workload, new VM, or any other change in one component can dramatically affect the performance of another. Problems that arise in one area can actually be symptoms of a problem rooted in another area altogether.

A classic example of this is the so-called "noisy neighbor" in which an issue in one VM, such as poor application performance, is actually caused by a different VM. Because of the large-scale, shared and dynamic nature of virtual environments, it can be difficult to understand and address even simple application and performance problems.

In a recent survey from SIOS Technology, more than half of the IT pros stated that they face application performance issues every month, and 44 percent indicated that it can take three hours or more to solve these problems. When these performance issues affect important business applications, they soon escalate to the top of the IT priority list, diverting valuable IT resources from far more productive activities.

IT teams that continue to struggle with finding the root causes of application performance issues with traditional threshold-based tools are wasting time and money. Instead, IT can recapture wasted time by using IT analytics tools capable of providing specific recommendations for solving issues, such as new machine learning based solutions.

IT staff can't afford to waste time by manually comparing results from multiple different tools to determine the status of their important applications and identify the causes of performance issues when they arise. IT personnel need to think about solutions that deliver more accurate, real-time insights into virtual environments, so they can keep the business humming and end users productive. Here are a few tips on how IT teams can avoid or resolve application performance issues quickly and easily:

Think Holistically About Your Infrastructure

In today's virtual environments, few issues are straightforward or confined to a single area of the infrastructure. According to a recent report, 78 percent of IT professionals are using multiple tools to identify the cause of application performance issues. The current strategy of relying on multiple tools and teams to evaluate each IT discipline or "silo" leaves IT with the manual, trial-and-error task of finding all the relevant data, assembling it and analyzing it to figure out what went wrong, what change may have caused the problem, and how best to fix it. IT needs to use IT analytics tools can look across the infrastructure siloes of application, network, storage, and VMs and allow you to take a holistic approach to finding the root causes of performance issues.

IT personnel don't have the time or resources to spend hours interpreting data or guessing at a solution. They need to be able to respond quickly to the real problem, or better yet, prevent the problem from occurring in the first place.

Predict and Avoid Problems Before They Happen

While fixing problems quickly is important, avoiding them in the first place is the real goal. Advanced machine learning and deep learning based analytics solutions are now able to actually predict potential performance problems before they happen and provide precise recommendations for avoiding them.

Replace Costly Over-Provisioning with Precise, Accurate Performance Optimization

Without an accurate and precise tool to identify the root causes of performance issues, many IT departments have resorted to costly over-provisioning. Simply throwing hardware at the problem may provide temporary performance improvements but rarely solves the problem permanently or provides the expected performance gains.

Few realize that new machine learning based IT analytics tools can provide a comprehensive analysis of their environment with recommendations for right-sizing it while maintaining (often improving) application performance. These tools provide a complete breakdown of current costs and performance compared with costs and performance gains that IT can realize with their recommended improvements.

With the right tools in place IT can waste less time sifting through alert storms and start focusing their energy on areas of the environment that are the root cause of their application performance issue.

Jerry Melnick is President and CEO of SIOS Technology.

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Ensuring Performance and Availability in a Virtual Environment

Jerry Melnick

Optimizing and ensuring performance and availability in a virtual (VMware) environment is far more challenging than the orderly and disciplined environment of dedicated physical servers. In virtual environments VMs, applications, storage, network, and other IT services share IT resources and operate in direct relationship to one another. A new or moved workload, new VM, or any other change in one component can dramatically affect the performance of another. Problems that arise in one area can actually be symptoms of a problem rooted in another area altogether.

A classic example of this is the so-called "noisy neighbor" in which an issue in one VM, such as poor application performance, is actually caused by a different VM. Because of the large-scale, shared and dynamic nature of virtual environments, it can be difficult to understand and address even simple application and performance problems.

In a recent survey from SIOS Technology, more than half of the IT pros stated that they face application performance issues every month, and 44 percent indicated that it can take three hours or more to solve these problems. When these performance issues affect important business applications, they soon escalate to the top of the IT priority list, diverting valuable IT resources from far more productive activities.

IT teams that continue to struggle with finding the root causes of application performance issues with traditional threshold-based tools are wasting time and money. Instead, IT can recapture wasted time by using IT analytics tools capable of providing specific recommendations for solving issues, such as new machine learning based solutions.

IT staff can't afford to waste time by manually comparing results from multiple different tools to determine the status of their important applications and identify the causes of performance issues when they arise. IT personnel need to think about solutions that deliver more accurate, real-time insights into virtual environments, so they can keep the business humming and end users productive. Here are a few tips on how IT teams can avoid or resolve application performance issues quickly and easily:

Think Holistically About Your Infrastructure

In today's virtual environments, few issues are straightforward or confined to a single area of the infrastructure. According to a recent report, 78 percent of IT professionals are using multiple tools to identify the cause of application performance issues. The current strategy of relying on multiple tools and teams to evaluate each IT discipline or "silo" leaves IT with the manual, trial-and-error task of finding all the relevant data, assembling it and analyzing it to figure out what went wrong, what change may have caused the problem, and how best to fix it. IT needs to use IT analytics tools can look across the infrastructure siloes of application, network, storage, and VMs and allow you to take a holistic approach to finding the root causes of performance issues.

IT personnel don't have the time or resources to spend hours interpreting data or guessing at a solution. They need to be able to respond quickly to the real problem, or better yet, prevent the problem from occurring in the first place.

Predict and Avoid Problems Before They Happen

While fixing problems quickly is important, avoiding them in the first place is the real goal. Advanced machine learning and deep learning based analytics solutions are now able to actually predict potential performance problems before they happen and provide precise recommendations for avoiding them.

Replace Costly Over-Provisioning with Precise, Accurate Performance Optimization

Without an accurate and precise tool to identify the root causes of performance issues, many IT departments have resorted to costly over-provisioning. Simply throwing hardware at the problem may provide temporary performance improvements but rarely solves the problem permanently or provides the expected performance gains.

Few realize that new machine learning based IT analytics tools can provide a comprehensive analysis of their environment with recommendations for right-sizing it while maintaining (often improving) application performance. These tools provide a complete breakdown of current costs and performance compared with costs and performance gains that IT can realize with their recommended improvements.

With the right tools in place IT can waste less time sifting through alert storms and start focusing their energy on areas of the environment that are the root cause of their application performance issue.

Jerry Melnick is President and CEO of SIOS Technology.

Hot Topics

The Latest

For years, infrastructure teams have treated compute as a relatively stable input. Capacity was provisioned, costs were forecasted, and performance expectations were set based on the assumption that identical resources behaved identically. That mental model is starting to break down. AI infrastructure is no longer behaving like static cloud capacity. It is increasingly behaving like a market ...

Resilience can no longer be defined by how quickly an organization recovers from an incident or disruption. The effectiveness of any resilience strategy is dependent on its ability to anticipate change, operate under continuous stress, and adapt confidently amid uncertainty ...

Mobile users are less tolerant of app instability than ever before. According to a new report from Luciq, No Margin for Error: What Mobile Users Expect and What Mobile Leaders Must Deliver in 2026, even minor performance issues now result in immediate abandonment, lost purchases, and long-term brand impact ...

Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...

Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...

AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...

More data center leaders are reducing their reliance on utility grids by investing in onsite power for rapidly scaling data centers, according to the Data Center Power Report from Bloom Energy ...

In MEAN TIME TO INSIGHT Episode 21, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses AI-driven NetOps ... 

Enterprise IT has become increasingly complex and fragmented. Organizations are juggling dozens — sometimes hundreds — of different tools for endpoint management, security, app delivery, and employee experience. Each one needs its own license, its own maintenance, and its own integration. The result is a patchwork of overlapping tools, data stuck in silos, security vulnerabilities, and IT teams are spending more time managing software than actually getting work done ...

2025 was the year everybody finally saw the cracks in the foundation. If you were running production workloads, you probably lived through at least one outage you could not explain to your executives without pulling up a diagram and a whiteboard ...