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

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 and machine learning in virtual infrastructuresThey 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.

Read Part 1

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

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 artificial intelligence and machine learning in virtual infrastructures

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.

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

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 techniques for stopping alert storms and dealing with a wide range of problems facing IT managers in VMware environments. View now.

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

View now

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

View the Webinar Now: Easy, Risk-Free Ways to Right Size Your VMware Environment

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.

  • vSphere Admin challenges and solutions
  • Complex relationships and how to identify root cause
  • Identify wasted resources and recouped costs
  • Machine learning and how it can help you
  • What VMs/Apps need SSD caching and what kind
  • Prevent problems before they happen and quickly solve them if they ever do

View the Webinar Now: Easy, Risk-Free Ways to Right Size Your VMware Environment

Are You Over Provisioning Your Virtual Infrastructure?

Right-Sizing VMware Environments with Machine Learning

According to leading analysts, today’s virtual data centers are as much as 80 percent overprovisioned – an issue that is wasting tens of thousands of dollars annually. The risks of overprovisioning virtual environments are urgent and immediate. IT managers face a variety of challenges related to correctly provisioning a virtual infrastructure. They need to stay within budget while avoiding downtime, delivering high performance for end-user productivity, ensuring high availability and meeting a variety of other service requirements. IT often deals with their fear of application performance issues by simply throwing hardware at the problem and avoiding any possibility of under-provisioning.  However, this strategy is driving costly over spending and draining precious IT time.  And even worse, when it comes time to compare the economics of on-premises hosting vs cloud, the costs of on-premises infrastructures are greatly inflated when the resources aren’t efficiently being used.  This can lead to poor decisions when planning a move to the cloud.

With all of these risks in play, how do IT teams know when their VMware environment is optimized?

Having access to accurate information that is simple to understand is essential.  The first step in right-sizing application workloads is understanding the patterns of the workloads and the resources they consume over time.  However, most tools take a simplistic approach when recommending resource optimization.  They use simple averages of metrics about a virtual machine.  This approach doesn’t give accurate information. Peaks and valleys of usage and interrelationships of resources cause unanticipated consequences for other applications when you reconfigure them.  To get the right information and make the right decisions for right-sizing, you need a solution such as SIOS Iq.  SIOS iQ applies machine learning to learn patterns of behavior of interrelated objects over time and across the infrastructure to accurately recommend optimizations that help operations, not hurt them.  Intelligent analytics beats averaging every time.

The second step towards a right-sizing strategy is eliminating the fear of dealing with performance issues when a problem happens or even preventing one in the first place.  This means having confidence that you have the accurate information needed to rapidly identify and fix an issue instead of simply throwing hardware at it and hoping it goes away.

Today’s tools are not very accurate. They lead IT through a maze of graphs and metrics without clear answers to key questions. IT teams typically operate and manage environments in separate silos — storage, networks, applications and hosts each with its own tools. To understand the relationships among of all the infrastructure components requires a lot of manual work and digging.  Further, these tools don’t deliver information, they only deliver marginally accurate data. And they require IT to do a lot of work to get that inaccurate data. That’s because they are threshold-based. IT has to set individual thresholds for each metric they want to measure –  CPU utilization, memory utilization, network latency, etc.. A single environment may need to set, monitor, and continuously tune thousands of individual thresholds. Every time the environment is changed, such as when a workload is moved or a new VM is created, the thresholds have to be readjusted. When a threshold is exceeded, these tools often create thousands of alerts, burying important information in “alert storms” with no root cause identified or resolution recommended.

Even more importantly, because these alerts are triggered off measurements of a single metric on a single resource, IT has to interpret the meaning and importance.  Ultimately the accuracy of interpretation is left to the skill and experience of the admin. When systems are changing and growing so fast and IT simply can’t keep up with it all- and the easiest course of action is to over-provision; wasting time and money in the process. Moreover, the actual root cause of the problem is often never fully addressed.

IT teams need smart tools that leverage advanced machine learning analytics to provide an aggregated, analyzed view of their entire infrastructure. A solution such as SIOS iQ helps to optimize provisioning, characterize underlying issues and identify and prioritize problems in virtual environments. SIOS iQ doesn’t use thresholds. It automatically analyzes the dynamic patterns of behavior between the related components in your environment over time. It automatically identifies a wide variety of wasted resources (rogue vmdks, snapshot waste, idle VMs). It also recommends changes to right-size all over- and under-provisioned VMs.

When it detects anomalous patterns of behavior, it provides a complete analysis of the root cause of the problem, the components affected by the problem, and recommended solutions to fix the problem. It not only recommends optimal provisioning of vCPU, vMem, and VMs, but also provides a detailed analysis of cost savings that its recommendations can deliver. Learn more about the SIOS iQ Savings and ROI calculator.

Here are three ways machine learning analytics can help avoid overprovisioning:

  1. Understand the causes of poor performance: By automatically and continuously observing resource utilization patterns in real-time, machine learning analytics can identify over- and undersized VMs and recommended configuration settings to right-size the VM for performance. If there’s a change, machine learning can dynamically update the recommendations.
  2. Reduce dependency on IT teams for resource sizing: App owners are often requesting as much storage capacity as possible, while VMware admins want to limit storage as much as possible. Machine learning analytics takes the guess work out of resource sizing and eliminates the finger-pointing that often happens among enterprise IT teams when there’s a problem.
  3. Eliminate unused or wasted IT resources: SIOS iQ will provide a saving and ROI analysis of wasted resources, including over-provisioned VMs, rogue VMDKs, unused VMs, and snapshot waste. It also provides recommendations for eliminating them and calculates the associated costs saving in both CapEx and Opex.
  4. Determine whether a cluster can tolerate host failure: With machine learning analytics, IT pros can easily right-size CPU and storage without putting SQL Server or end user productivity at risk. IT teams gain a deeper understanding into the capacity of the organization’s hosts and know whether a cluster can tolerate failure or other issues.

To learn more about how right-sizing your VMware environment with machine learning can save time and resources, check out our webinar: “Save Big by Right Sizing Your SQL Server VMware Environment.

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

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.

Roadblocks to Optimizing Application Performance in VMware Environments – Part II

This is the second blog post in a two-part series examining challenges IT teams face in optimizing application performance and other issues in VMware environments. The original text of this series appeared in an article on Data Informed.  

In part one of this series, we uncovered that IT teams are currently using multiple tools to understand application performance issues in VMware. Read on to learn about the other challenges IT teams are facing in virtual environments

Application Performance Issues Are Eating Away at Time and Resources

While IT professionals are consulting their VMware environment application 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.Application Performance Labor Hours

These interruptions are significant, considering that our recent survey 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.

The Causes of Application Performance Issues Remains 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 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.

What’s Next?

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 are 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, Gartner estimates only five percent of businesses currently have AIOps platforms in place. However, that number is expected to mushroom to 25 percent in the next two years as IT becomes increasingly complex and difficult to manage.

Read Roadblocks to Optimizing Application Performance in VMware Environments part one

Expert Advice on High Availability SQL Server and Machine Learning Analytics – Blogs, Webinars and Live Events

SIOS experts in high availability SQL Server cluster protection routinely share their knowledge and provide expert advice through webinars, blogs, and live events.

Blog Post: Step-by-Step Guide to High Availability SQL Server v.Next Linux High Availability
Click here to learn how to deploy a Linux VM in Azure running SQL Server and how to configure a 2-node failover cluster to make it highly available without the need for shared storage. https://clusteringformeremortals.com/category/high-availability/

Blog Post:  Deploying a Highly Available File Server in Azure IAAS (ARM) with SIOS DataKeeper This blog is a step-by-step guide to deploying a two-node File Server Failover Cluster in a single region of Azure using Azure Resource Manager.

Live Webinar: December 15, 2016 –  Webinar: Keeping the Peace with Your Sys Admins while Providing High Availability SQL Server. Join SQL Server MVP Dave Bermingham as he walks through common use cases that cause conflict between SQL DB Admins and VM Admins when SQL Server HA is involved. He will also describe a simple, conflict-free way for SQL Server admins to get what they need for a successful implementation of SQL Server. Register here.

Live Webinar: December 8, 2016 – Understanding vSphere Analytics: Machine Learning vs Threshold-basedJoin David Davis, 8-time vExpert and Partner at ActualTechMedia and Experts from SIOS Technology in this real-world, practical, hands-on webinar! Find out the differences between different vSphere analytics approaches and how to choose a solution that’s best for you. Register here.

Recorded Webinar – VMware Guest Based SQL Server High Availability Clusters – Ways to Protect SQL and Maintain Flexibility. Watch this recorded webinar and hear Dave Bermingham, Microsoft Clustering and Datacenter MVP and Tony Tomarchio, Director of Field Engineering at SIOS discuss ways to create a high availability cluster to protect SQL in a VMware environment without sacrificing IT flexibility or important VMware features, and whether you can have a cluster and multi-site replication with VMware. Register here.

Optimize SQL Server Performance and 5 Other Things to Do at VMworld 2016

#1. Experience an Augmented Reality Guided Tour of SIOS iQ Machine Learning Analytics Solution for SQL Server Performance

Root cause of SQL Server performance issues with SIOS iQ machine learning analytics
SIOS iQ machine learning analytics with enhanced reality headset.

Try to improve your SQL Server performance in this fun augmented reality demo. See how you can save money by solving performance issues in one click. Identify wasted virtual resources.  Optimize your VMware application environment.

#2. Attend Breakout Session

SIOS CTO Sergey Razin, Ph.D., and SQL Sentry’s Brian Davis present – Use Cases in Performance Root Cause Resolution for SQL Server. They show you how to use vRealize Operations Manager, SIOS iQ Analytics, and SQL Sentry Performance Advisor. Learn best practices for identifying and resolving application performance issues in SQL Server. Identify SQL Server performance issues using VMware vRealize Operations Manager, characterize infrastructure root causes and recommended solutions with SIOS iQ machine learning analytics solutions, and define application-specific root causes with SQL Sentry Performance Advisor.  9/1/2016 for Use Cases in SQL Server Performance Root Cause Resolution.


#3.  Stop by SIOS Speaking Session in Tegile Booth #2057 to Learn Keys for Implementing Cost-Efficient Storage Accelerationtegile performance

SIOS Director of Field Engineering will be a guest presenter in the
Tegile booth #2057. He will talk about using machine learning analytics for a successful storage acceleration strategy. He will show how to use it to accelerate SQL Server performance.


#4.  Stop by Book Signing with vExpert Michael Corey in SIOS Booth #2361corey book

VMware vExpert and noted author Michael Corey will be in the SIOS booth on August 30 to sign his book Virtualizing SQL Server with VMware: Doing IT Right. The first 10 attendees to arrive at the book signing will receive a free copy. The book is also available in the show bookstore.


#5. Join Guest Presenter: SQL Sentry in SIOS Booth #2361 for Demo of Integration of SIOS iQ and SQL Sentry Performance Advisor

SQL_Sentry

Stop by SIOS Booth #2361 on Monday, August 29 at 2:00 PM-3:00 PM and Tuesday, August 30 at 11:00 AM to noon to see a demonstration by SQL Sentry’s Brian Davis for a live demonstration of integration of SIOS iQ with SQL Sentry Performance Advisor. Learn how this integration bridges a critical gap between IT infrastructure administrators and SQL Server administrators. See how easy it is to find and resolve SQL Server performance issues with SIOS iQ and SQL Sentry.

It’s Time To Demystify Quorums

As I read through the SQL Server forums and field questions from IT pros, it surprises me how many are simply off-put by the mention of the word quorum. It’s a six-letter word treated like a part of our collective four letter vocabulary.

Quorum Defined: A voting mechanism to ensure correct ownership of cluster resources.A “quorum” in an IT-sense is simply a voting mechanism to ensure the correct ownership of a cluster. Most commonly, it’s used in conjunction with Always On (both Failover Cluster Instances and Availability Groups), Hyper-V Clusters and all Windows Server Failover Clusters.

The key isn’t to know where to use them it’s how to use them. I put together a short 30 minute webcast that dives into the a variety of quorum types most commonly used — including the pros, cons, and illustrations to demystify quorums and, dare I say, make them easy to understand. I also suggest tuning in after the 30 minute mark — we had some great questions come in as part of the Q&A.

View the webinar now

Additional Resources