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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

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

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According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

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