Archives for April 2017

Part 2- AI: It’s All About the Data: The Shift from Computer Science to Data Science

April 14, 2017 by sios2017 Leave a Comment

This is the second post in a two-part series. Part One is available here. We are highlighting the shifting roles of IT with the emergence of machine learning based IT analytics tools.

Machine Learning Provides the Answers

The newest data science approach to managing and optimizing virtual infrastructures applies the AI discipline of machine learning (ML).

Rather than monitoring individual components in the traditional computer science way, ML tools analyze the behavior of interrelated components. They track the normal patterns of these complex behaviors as they change over time. Machine learning-based analytics tools automatically identify the root causes of performance issues and recommend the steps needed to fix them.

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?

Unlike earlier analytics tools were general purpose or provided relatively low-level primitives or APIs, leaving IT to determine how to apply them for specific purposes. Early tools were largely impractical because they had limited applicability. Moreover, IT pros using them had to have a deep analytical background. New tools are much different. They allow IT pros to leapfrog ahead -to use advanced data science approaches without specialized training. Artificial Intelligence They automatically deliver fast, accurate solutions to complex problems like root cause analysis, rightsizing, or capacity planning.

First, IT will shift their emphasis from diagnosing problems to avoiding them in the first place. Next, freed of the need to over-provision to ensure performance and reliability, they will look for ways to optimize efficiency. Finally, they will use ML tools to implement strategies to evolve and scale their environments to support their business’s operations.

And as IT pro’s mature their understanding and use of machine learning-based analytics tools, they will be on the forefront of building the foundation for automation and the future of the self-driving data center.

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Part 1: AI is All About the Data: The Shift from Computer Science to Data Science

April 10, 2017 by sios2017 Leave a Comment

This is the first post in a two-part series. Part 2 is available here. We are highlighting the shifting roles of IT as artificial intelligence (AI) driven data science evolves.

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. 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. What’s more, businesses are seeing the value in dedicating budget and resources to leverage artificial intelligence, specifically machine learning, and deep learning. They are using this powerful technology to analyze this data to increase efficiency and performance.

Data Science to the Rescue

The complexity of managing virtual IT environments is stressing out traditional IT departments. However, IT pros are discovering that the solution lies in the data and in the artificial intelligence-based tools that can leverage it. Most are in the process of understanding how powerful data is in making decisions about configuring, optimizing, and troubleshooting virtual environments. Early stage virtualization environments were monitored and managed in the same way physical server environments were. That is, IT pros operated in discrete silos (network, storage, infrastructure, application). They used multiple threshold- based tools to monitor and manage them focusing on individual metrics – CPU utilization, memory utilization, network latency, etc. When a metric exceeds a preset threshold, these tools create alerts – often thousands of alerts for a single issue.

If you compare a computer science approach to a data science (AI) approach, several observations become clear. IT based the traditional approach on computer science principles that they have used for the last 20 years. This threshold-based approach originated in relatively static, low-volume physical server environments. IT staff analyze individual alerts to determine what caused the problem, how critical it is, and how to fix it. However, unlike physical server environments, components in virtual environments are highly interdependent and constantly changing. Given the enormous growth of virtualized systems, IT pros cannot make informed decisions by analyzing alerts from a single silo at a time.

Artificial Intelligence, Deep Learning, and Machine Learning

To get accurate answers to key questions in large virtualized environments, IT teams need an artificial intelligence -based analytics solution. They need a solution capable of simultaneously considering all of the data arising from across the IT infrastructure silos and applications. In virtual environments, components share IT resources and interact with one another in subtle ways. You need a solution that understands these interactions and the changing patterns of their behavior over time. It should understand how it changes through a business week and as seasonal changes occur over the course of a year. Most importantly, IT needs AI-driven solutions that do the work for IT. It should identify root causes of issues, recommend solutions, predict future problems, and forecast future capacity needs.

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Stopping Alert Storms and Finding Root Causes of Performance Issues in VMware vSphere Infrastructures with Machine Learning

April 8, 2017 by sios2017 Leave a Comment

View this recorded webinar to hear noted vExpert and principle analyst for ActualTech Media, David M. Davis, and Jim Shocrylas, SIOS Technology’s Director of Product Management discussing a wide range of problems and recommended solutions facing IT managers in VMware environments.

David discusses the changes in IT that led to the creation of the IO “blender” that we see today and the ways traditional threshold-based monitoring and management tools are falling short. He reviews the challenges this situation poses for IT managers who are trying to solve problems, eliminate wasted resources, and meet service levels – from overwhelming alert storms, to “siloed” view of the infrastructure, to inefficient (and costly) trial-and-error problem-solving.

He discussed the ways new machine learning-based IT analytics are answering the questions that traditional threshold-based solutions cannot – what is the root cause of the problem and how to fix it. Jim Shocrylas provides a demo of SIOS iQ machine learning analytics solution and shows how easy it is to:

Be aware of important issues without alert storms
Identify root causes of performance issues quickly, easily, and accurately
Right-size performance and capacity in vSphere infrastructures without risk
Prevent problems before they happen

Webinar Explains How to Eliminate Over Sizing in Virtual Environments without Risking Application Performance

April 4, 2017 by Margaret Hoagland Leave a Comment

April 6th at 2:00 PM Eastern/11:00 AM Pacific

Register Here
According to experts, virtual environments are over-provisioned by as much as 80%. IT is wasting tens of thousands of dollars a year on hardware, software, and IT time that doesn’t benefit the company. Without an effective way to see across the virtual infrastructure silos and into the interactions between components, IT is blind-sided by performance issues, capacity over-runs, and other unexpected consequence. As more important applications are being moved into virtual environments, the pressure is even greater to deliver uninterrupted high performance and any cost. This limited view into virtual infrastructures is also causing IT to keep unnecessary snapshots, rogue VMDKs, and idle VMs. In this webinar, ActualTech Founder and noted vExpert, David Davis and SIOS’s director of product management, Jim Shocrylas discuss simple solutions to right-sizing virtual environments that are possible with machine learning based analytics.

Join this webinar to learn how machine learning based analytics solutions are delivering the precise, accurate information you need to right size your virtual environment without risking performance or availability.

Watch a demonstration of a machine learning based analytics tool about how to eliminate application performance issues, configure virtual resources for optimal performance and efficiency, and forecast performance requirements.