When virtual computing first became popular, it was primarily used for non-business critical applications in pre-production environments, while critical applications were kept on physical servers. However, IT has warmed up to virtualization, recognizing the many benefits (reduced cost, increased agility, etc.) and moving more business-critical and database applications into virtual environments. A recent survey conducted by SIOS Technology Corp. of 518 IT professionals found that 81 percent of respondents are now running their business-critical applications, including SQL Server, Oracle, or SAP, in their VMware environments.
While virtualized environments have numerous benefits, they introduce a new set of challenges for IT professionals. For IT teams tasked with finding and resolving performance issues in these environments, specifically those that can impact business-critical applications, many find they are hitting the same cumbersome roadblocks related to tools, time, and strategy.
Too Many Tools Bring Rising Complexity
According to the SIOS report, 78 percent of IT professionals are using multiple tools — including application monitoring, reporting, and infrastructure analytics — to identify the cause of application performance issues in VMware. Furthermore, ten percent of IT professionals are using more than seven tools to understand their VMs. Optimizing performance and availability in VMware environments is incredibly complex, and the dynamic nature of these environments require highly advanced tools to address even the most standard performance issues.
But relying on several reporting tools every time an issue arises just isn’t sustainable for most IT teams. This is partly due to the fact that solving application performance issues requires a view of multiple IT disciplines, or “silos,” such as application, network, storage, and compute. In larger organizations, that means each time an issue arises, representatives from each discipline need to come together and compare their findings — and the analysis results from the application team’s tool may point to a somewhat different cause than that of the storage team or the network team. The current strategy of relying on multiple tools and teams to evaluate each 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 and what changed to cause the problem.
Application Performance Issues Eat Away at Time and Resources
While IT professionals are consulting their VMware environment monitoring tools, critical hours are ticking by. For smaller businesses that have limited IT staff, this can cause considerable delays in day-to-day operations. IT teams cannot afford to waste time chasing false positives or focusing their energy on areas of the environment that are not truly the root cause of their application performance issue. Additionally, many IT teams are inundated by alerts from their VMware environment monitoring tools, making it difficult to pinpoint which alerts are meaningless and which are worth diagnosing to solve a potential application performance issue.
These interruptions are significant, considering that the SIOS report found more than half of IT professionals are facing applications performance issues every month. Additionally, 44 percent indicated that it takes them more than three hours to resolve application performance issues as they arise. Overall, it’s clear that IT teams are frequently facing issues in VMware environments, and they are wasting critical manpower and resources solving these issues.
Root Causes Often Remain a Mystery
Despite the wide variety of tools available and the volume of time spent solving business-critical application performance issues, IT professionals remain uncertain they can attack these problems head-on. Of the IT professionals SIOS surveyed, only 20 percent believe the strategies they implement to resolve application performance issues are 100 percent accurate the first time. Even more alarming, seven percent would characterize their application performance issue resolutions as an “educated guess.” And across the board, it is rare for IT teams to implement a perfect solution to a performance issue — they frequently require a level of adjustment, or even a complete rework.
This trend towards moving business-critical data off of physical servers and onto virtual environments will continue for the foreseeable future, and the relationships between VM applications, network devices, storage devices, and services will only grow more complex. Many CIOs are turning to machine learning solutions to help them better understand their infrastructure and learn to optimize the relationships that exists between the different IT disciplines. As a result, the core approach used by IT professionals is changing from a traditional computer science approach to a data science-centric approach. We’ve also seen the rise of “AIOps,” or algorithmic IT operations platforms, in the last year. A term coined by Gartner to describe machine learning applications in IT, AIOps is estimated to currently be in place in only five percent of businesses. However, that number is expected to mushroom to 25 percent in the next two years as IT becomes increasingly complex and difficult to manage.
Jerry Melnick, President and Chief Executive Officer, SIOS Technology Corp.
Jerry is responsible directing the overall corporate strategy for SIOS Technology Corp. and leading the company’s ongoing growth and expansion. He has more than 25 years of experience in the enterprise and high availability software markets. Before joining SIOS, he was CTO at Marathon Technologies where he led business and product strategy for the company’s fault tolerant solutions. His experience also includes executive positions at PPGx, Inc. and Belmont Research where he was responsible for building a leading-edge software product and consulting business focused on supplying data warehouse and analytical tools. He holds a Bachelor of Science degree from Beloit College with graduate work in Computer Engineering and Computer Science at Boston University.
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