|Here is a weekly roundup of the latest news, events, and free resources available from SIOS.|
VISIT SIOS AT AN UPCOMING EVENT
|August 27-31 – VMworld Las Vegas, Mandalay Bay – Booth #1518
Join us for SIOS PERC Bingo! Enter to win a Bebop drone! See a demo of exciting new features in SIOS iQ machine learning analytics platform for VMware
|September 25-28 – .conf Splunk 2017; Washington DC
Stop by the SIOS Booth to learn more about SIOS iQ Machine Learning Analytics
|September 25-29 – SAP TechEdge; Las Vegas
See how easy it is to provide high availability cluster protection for SAP in physical, virtual, and cloud environments.
|Oct/Nov – VMUG Conferences:
Visit SIOS at a VMUG near to you to see a demonstration of SIOS iQ machine learning analytics platform. Enter to win a raffle prize.
|October 31-Nov 3 – PASS Summit; Washington Convention Center
Visit the SIOS booth for a demonstration of SIOS DataKeeper for high availability protection of SQL Server in physical, virtual, and cloud environments.
|November 2 – PASS The Bacon Sponsored breakfast & panel discussion Rm 602-604
Our exciting annual event. SQL Server Experts sharing their Tales from the Field: HA in the Cloud: Denny Cherry, Joey D’Antoni and Geoff Hiten moderated by Dave Bermingham
NEW WHITE PAPERS AND WEBINARS
The volume/scope of data that businesses produce and IT pros have to manage has grown rapidly. Managing this onslaught of data can hinder IT productivity, especially when they are hampered by traditional tools and old school approaches. The exponential growth of virtual IT infrastructures in both scale and complexity is pushing IT teams to their limits. However, IT teams are still looking at their virtual infrastructures in individual operational silos – compute, application, storage, and network.
They are using multiple tools to gather information about each silo and then piecing the results together manually. They rely on their own experience to develop a theory about the root cause of performance issues and to devise a strategy for resolution. This inaccurate approach is leaving IT time strapped, stressed out and without clear answers to key questions about application performance issues in virtualized environments, including how to fix them. More and more companies are looking to machine learning based solutions for the answer.
Given the enormous growth of virtualized systems, IT pros can no longer make informed decisions by analyzing alerts from traditional threshold-based analytics and monitoring tools. Similar to the manufacturing revolution of the past, IT pros now need help from machines to be effective in today’s data-driven world. They need a solution capable of simultaneously considering data from across the IT infrastructure silos and applications. A solution that understands the subtle ways that components in virtual environments interact with one another and the changing patterns of their behavior over time. Most importantly, IT pros need advanced machine learning and deep learning tools that do this work for them.
Machine Learning / AI Debate
While debate continues over the effect that machine learning and AI will have on the workforce, there is no denying that IT needs help from machines. This particularly true for IT teams that are managing virtual environments. Machine learning is here to relieve IT of low-value manual work, not replace them. Machine learning analytics tools provide a degree of automation, precision, and accuracy that humans with threshold-based tools cannot approximate. In one click of an advanced ML-based solution like SIOS iQ, they can identify root causes of issues, predicting future problems, and get recommended solutions.
IT pros shouldn’t have to weed through hundreds of alerts or compare dashboards filled with charts to diagnose problems. It’s time-consuming and ineffective. With new, advanced machine learning and deep learning-based tools, IT teams can move from a reactive to a proactive approach. They can shift their emphasis from diagnosing problems to avoiding them in the first place. Freed from the need to over-provision in order to ensure performance and reliability, they can look for ways to optimize efficiency and responsiveness in their data center. They can also use machine learning tools to implement strategies that evolve their environments to support their business’s operations.
The Benefit of Machines Learning /AI
Machine learning enables IT pros deliver fast, accurate solutions to complex problems. IT can spend more time for innovating and less time on trial-and-error. Fortunately, advanced solutions like SIOS iQ, can help IT optimize provisioning, identify performance issues and prioritize problems instantaneously. SIOS iQ learns patterns of behavior of interrelated objects over time and across the infrastructure. It uses patented meta-analysis techniques to predict issues before they arise and recommend precise solutions.
When end-users report slow performance in business-critical applications, IT teams everything to fix the problem as quickly as possible. In virtual environments, where the root causes of problems are rarely straightforward, they may spend days trying and testing multiple different solutions. Troubleshooting this way creates a huge drain on IT time and resources – and even occasionally, morale. IT teams want to be innovators who add value to their business operations with new technology that automate manual tasks, increase end user productivity, streamline costs and respond to business needs quickly and flexibly. Unfortunately, without the insights and automation that machine learning analytics provides, IT departments are wasting more and more time and resources on low-value problem-solving.
Virtual Infrastructures are Too Complex
for One-Dimensional Approaches
What is causing this problem-solving quagmire? IT is running more business critical applications in complex, dynamic virtual infrastructures where traditional diagnostic and monitoring tools cannot identify root causes of application performance issues or provide specific steps to solve them. IT teams are still looking at their virtual infrastructures in individual operational silos – compute, application, storage, and network. They are using multiple tools to gather information about each silo and then piecing the results together manually to devise a theory about the root cause and a strategy for resolution.
Threshold-based Tools and Old-School Approaches
In a recent survey SIOS conducted, 78 percent of respondents are using multiple tools to identify the cause of application performance issues in VMware. Only 20 percent of respondents said the strategies they are using to resolve these issues is completely accurate the first time.
Legacy monitoring tools use threshold-based technology that was originally developed for physical server environments. They help you keep physical components operating within specific parameters, such as CPU utilization, storage latency, and network latency. You manually set the parameter thresholds for every metric you want to monitor in every silo and these tools will alert you every time a threshold is exceeded – often hundreds of times for a single incident.
More Data is Not More Information
In virtual environments, virtual resources share the physical host, storage, and network resources. These components work together in complex interrelationships that often mask the root causes of performance issues. IT pros responsible for each silo have to decipher hundreds of alerts and pinpoint what matters using their subjective opinions and good old trial and error.
Fortunately, new machine learning analytics solutions like SIOS iQ use deep learning techniques to look across the silos, factor in the interrelationships of virtual resources, and identify the root causes of application performance issues. They use predictive analytics technology to identify the issues that will cause performance issues in the future so you can avoid them. They provide a degree of automation, precision, and accuracy that humans with threshold-based tools cannot approximate.
Machine Learning Analytics Eliminates Trial and Error
Machine learning analytics tools tell you how to resolve the issues. You don’t need to weed through hundreds of alerts or compare dashboards filled with charts to diagnose the problem. You get the info you need without the expertise of a data scientist. With machine learning analytics, there is no need for data selection, modeling, preparation, extraction or configuration is necessary. SIOS iQ tells IT which infrastructure anomalies are important and which are minor so they can prioritize their valuable time.
With new and advanced machine learning and deep learning tools, IT teams can move from a reactive to proactive state. That means you can spend more time innovating and less time on trial-and-error.
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. 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.
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.