IT Briefcase Exclusive Interview with SIOS Technology: Machine Learning Applications in IT

In this interview, Jerry Melnick, president and CEO of SIOS Technology, discusses how IT teams are adopting machine learning analytics tools to proactively fight application performance issues.

• Q: What changed within IT that created a renewed need for machine learning based IT analytics?

A: Right now, businesses are moving more business critical applications to virtual environments that require consistent high performance. Although virtual IT environments are more complex than traditional on-premises environments, companies are still using an on-premises “siloed” approach to manage them. That is, the IT infrastructure and the teams that manage them are siloed into network, application, compute and storage. Each group uses a different collection of monitoring and diagnostic tools, making it difficult to find the root of application performance issues. This means that IT is now overwhelmed with time-consuming problem solving. Many in IT feel that they’ve becoming reactive firefighters solving the same problem repeatedly, wasting their valuable time, and are unable to use IT resources to their full potential.

• Q: Was IT slow to adopt machine learning technology?

A: It wasn’t. In fact, IT has been adopting many different tools to help solve the problem. The problem is that most technologies, even those with some machine learning capabilities, have been designed to look down the same siloes as traditional tools. They don’t account for the complex interactions that happen between components in a virtual infrastructure where physical resources are shared across multiple virtual machines.

IT should be using tools that apply advanced machine learning and deep learning technology to understand the complex patterns of behavior between these interrelated components over time, without regard to traditional silos. With this understanding, these tools can apply deep learning techniques to precisely identify the root causes of performance issues, and provide specific recommendations for resolving them. They can also accurately predict the emergence of a performance issue a week or more into the future, and recommend specific steps to avoid the issue in the first place.

• Q: What does the term “AIOps” mean?

A: “AIOps” is a term Gartner coined last year to describe machine learning applications in IT. Gartner estimates that 5% of organizations currently have an AIOps platform in place, but that number is expected to rise to 25% over the next two years.

• Q: What does a machine learning-based IT analytics platform actually look like to IT?

A: When end users report an application performance issue, IT teams from each of the different silos look at problem from their own narrow perspective, using a multiplicity of diagnostic tools. Some tools make primitive attempts at providing a broader view of the infrastructure by providing “dashboards” filled with multiple charts. This means IT is left to rely on crude comparisons of time stamps and charts, and their own subjective understanding of the situation to determine the likely cause. Finding the root cause of an issue in this way is extremely labor intensive, highly dependent on human judgement; and in most cases, requires rework later on.

Machine learning technology enables IT to analyze data from a wide variety of sources – across the silos, and to account for the complex patterns of behavior between interrelated objects over time. Tools that use advanced machine learning and deep learning technology instantly identify the root cause of performance issues and provide recommendations for solving them with a level of precision and accuracy that humans alone cannot provide.

• Q: What part of IT benefits the most from machine learning-based analytics technology?

A: Machine learning based IT analytics platforms can enable IT to act proactively, instead of reactively. This means IT can avoid problems before they happen, add new workloads without disruption, identify the optimal configuration for storage acceleration, eliminate over provisioning and other wasted resources without jeopardizing performance

It also means that IT gains valuable time and the power to implement new workloads and configurations that add value to the core business of the company. For example, the insights and recommendations offered by these tools can enable an IT department to add applications quickly to increase the business’ agility in their market, and reduce the cost of doing business.

• Q: Do machine learning-based solutions replace other IT tool?

A: A better question is – how can machine learning-based solutions enhance what IT is doing today? The more advanced solutions are providing an IT analytics platform that is capable of integrating data from a wide range of sources – from infrastructure hardware, application monitoring software, data fabric tools, etc. – to provide a holistic view of the virtual infrastructure. Instead of forcing IT to choose one tool over another, or to compare the results, this platform approach, analyzes and integrates all of the data provided by these tools into a precise, accurate, complete view into status of the infrastructure.

• Q. How will machine learning transform the traditional IT role? Will it replace jobs?

A: Machine learning gives IT their jobs back. By providing proactive insights into application performance issues, IT can solve issues before they even arise. And they can do so without costly over-provisioning and time-consuming trial-and-error efforts. Machine learning based analytics tools aren’t replacing jobs, they are enabling them to add more value to their organization’s success.

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.