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Subscribe to Data Informed The IT Shift from Computer Science to Data Science

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You may think that the words “artificial intelligence” or “machine learning” sound like trendy buzzwords. In reality, much of the hype about this technology is true. Unlike past periods of excitement over artificial intelligence, today’s interest is no longer an academic exercise. Now, IT has a real-world need for faster solutions to problems that are too complex for humans alone. This includes identifying the root causes of performance issues in virtual infrastructures.

Today, almost every large enterprise has virtualized part, or all, of their data centers. With virtualization, IT teams gain access to a huge variety and volume of real-time machine data they want to use to understand and solve the issues in their IT operations environments. However, the complexity of managing virtual IT environments is stressing out traditional IT departments. As a result, IT pros are discovering that the solution lies in the data and in the artificial intelligence-based tools that can leverage it.

Data Science to the Rescue

As worldwide digital data levels continue to climb, companies are working to find the business value in their data, and to adapt their computer science strategies to the evolving data science market. Legacy management and monitoring tools used the same approach they used for physical server environments — that is, by looking at discrete silos (network, storage, infrastructure, application). They used multiple manually-set thresholds to focus on individual metrics — CPU utilization, memory utilization, network latency, etc., within each silo.

This threshold-based approach originated in a relatively static, well-understood physical server environment which has proven ineffective in handling the complexity of today’s virtual environments. Unlike their counterparts in physical server environments, components in virtual environments share host resources, creating complex, highly interdependent relationships between them. They are also highly dynamic, enabling IT to continually create and move workloads across VMs. IT pros can no longer make informed decisions using manual computer science approaches of yesterday and analyzing alerts from a single silo at a time. This is why companies are turning to “data science” approach that leverages sophisticated AI disciplines of machine learning and deep learning to get a holistic, automated solution to eliminate the time-consuming, manual process of problem-solving performance issues and optimizing virtual environments.

Machine Learning Analytics Tools Provide the Answers

Rather than monitoring individual metrics as threshold-based tools do, advanced machine learning-based solutions learn the complex behavior of interrelated components as they change over time. They can consider multiple metrics of related components simultaneously. As a result, they deliver much more precise, accurate information about virtual environments than either primitive machine learning tools or traditional threshold-based tools. Instead of creating “alert storms”, they identify the meaningful incidents associated with abnormal behavior at a specific time of day, week, month and year. And because machine learning is central to the design, there is no manual configuration required. Advanced machine learning solutions, can be up and running in minutes and learning behaviors immediately. As a result, this shift to a data-centric, behavior-based approach has major implications that significantly empower IT professionals. IT pros will always need domain expertise in computer science, but what analytical skills will IT need to become effective in this new AI-driven world?

Instead of spending their days reacting to and reworking application performance issues, IT will shift their focus from diagnosing problems to proactively predicting and avoiding them in the first place. Freed of the need to over-provision to ensure performance and reliability, they will be able to look for ways to optimize efficiency and spend their time focusing on the larger goals at hand. This allows IT to provide true business value, and work on projects that drive company objectives forward. Generally, that kind of value gives IT an important voice in senior management, bringing them into the decision making process and closing the gap between IT and operations. And as IT pros understanding and use of machine learning-based analytics tools advance, they will be on the forefront of building the foundation for automation and the future of the self-driving data center.

Jim’s Bio:

Jim Shocrylas is the Director of Product Management at SIOS.  Jim has more than 20 years in the IT industry, most recently as Portfolio Manager for EMC’s Emerging Technologies Division.

Understanding The Emerging field of AIOps – Part II

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This is the second post in a two-part series highlighting how AIOps is changing IT performance optimization. Part 1 explained the basic principles of AIOps. The original text of this series appeared in an article on Information Management.  Here we look at the business requirements driving the trend to AIOps.

Why do businesses need AIOps?

IT pros move more of their business-critical applications into virtualized environments. As a result, finding the root cause of application performance issues is more complicated than ever.  IT managers have to find problems in a complex web of VM applications, storage devices, network devices and services. These components that are connected in ways IT can’t always understand.

Often, the components a VMware or other virtual environment are interdependent and intertwined. When an IT manager moves a workload or makes a change to one component, they cause problems in several other components without their knowledge. If the components are in different so-called silos (network, infrastructure, application, storage, etc.), IT pros have even more trouble figuring out the actual cause of the problem.

Too Many Tools Required to Find Root Causes of Performance Issues

AIOPs Survey
SIOS AIOPS Survey

The process of correlating IT performance issues to its root cause is  difficult, if not impossible for IT leaders.  According to a recent SIOS report, 78 percent of IT professionals are using multiple tools to identify the cause of application performance issues in VMware. For example, they are using tools such as application monitoring, reporting and infrastructure analytics.

Often, when faced with an issue, IT assembles a team with representatives from each IT silo or area of expertise. Each team member uses his or her own diagnostic tools and looks at the problem their own silo-specific perspective. Next, the team members compare the results of their individual analyses identify common elements. Frequently, this process is highly manual. They look at changes in infrastructure that show up in several analyses in the same time frame. As a result, IT departments are wasting more and more of their budget on manual work and inaccurate trial-and-error inefficiencies.

To solve this problem and reduce wasted time, they are using an AIOPs approach. AIOps applies artificial intelligence (i.e., machine learning, deep learning) to automate problem-solving. The AIOPs trend is an important shift away from traditional threshold-based approaches that measure individual qualities (CPU utilization, latency, etc.) to a more holistic data-driven approach. Therefore, IT managers are using analytics tools to analyze data across the infrastructure silos in real-time. They are using advanced deep learning and machine learning analytics tools that learn the patterns of behavior between interdependent components over time.  As a result, they can automatically identify behaviors between components that may indicate a problem. More importantly, they automatically recommend the specific steps to resolve problems.

What’s Next for AIOps?

Virtual IT environments are creating an enormous volume of data and an unprecedented level of complexity. As a result, IT managers cannot manage these environments effectively with traditional, manual methods. Over the next few years, the IT profession will rapidly move from the traditional computer science approach to a modern “data science” AIOPs approach. For IT teams, this means embracing machine learning-based analytics solutions, and understanding how to use it to solve problems efficiently and effectively. Finally, executives need to work with their IT departments to identify to right AIOps platform for their business.

Read Part 1

What You Need to Know About the Emerging field of AIOps – Part 1

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This is the first post in a two-part series. We are highlighting how AIOps is changing IT performance optimization. The original text of this series appeared in an article on Information Management.

During the next two years, companies are set to spend $31.3 billion on cognitive systems tools. Today, companies are using tools based on these technologies (i.e., data analytics and machine learning) to solve problems in a wide range of areas. For example, companies are using artificial intelligence (AI)-powered customer service bots and trucking routes that data scientist design. Ironically, information technology (IT) departments have not yet fully leveraged the power of machine-learning based analytics — IT.

Survey Shows More Critical Apps in VMware

HoweAIOPs Surveyver, that is changing because IT environments are becoming increasingly complex. They are moving from physical servers to virtual environments. According to a recent study from SIOS Technology, 81 percent of IT teams are running business-critical applications in VMware environments.

Virtual environments are made up of components, such as VMs, applications, storage and network that are highly interrelated and constantly changing. To manage and optimize these environments, IT managers have to analyze an enormous volume of data. They learn the patterns of behavior between component. This lets them accurately correlate application service issues to the root cause of the problem in the virtual environment.  As a result, a new field has emerged – AIOps.

What is AIOps?

AIOps (algorithmic IT operations platforms) is a new term that Gartner uses to describe the next phase of IT operations analytics. These platforms use machine learning and deep learning technology to automate the process of finding performance issues in IT operations.

Right now, Gartner estimates only five percent of businesses have an AIOps platform in place. However, more businesses will adopt these platform during the next two years, bringing that number to 25 percent. Importantly, AIOps replaces human intelligence with machine intelligence. It deciphers interactions within virtual IT environments. Consequently, they can uncover infrastructure issues, correlate them to application operations problems and recommend solutions.

AIOps platforms use machine learning to understand how these environments behave over time to identify abnormal behavior. Furthermore, IT can even use AIOps platforms to find and stop potential threats before they become application performance issues.