|April 10, 2017||
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.
|April 8, 2017||
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 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:
|April 4, 2017||
Webinar Explains How to Eliminate Over Sizing in Virtual Environments without Risking Application Performance
April 6th at 2:00 PM Eastern/11:00 AM Pacific
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.
This live webinar is interactive so bring your questions.
|March 1, 2017||
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:
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.”
|February 23, 2017||
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
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