|June 6, 2017||
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
|May 15, 2017||
AWS Quick Start Templates Deploy SQL High-Availability Failover Cluster in the Cloud
Many businesses are struggling to deploy a high-availability failover cluster for SQL Server and other important applications in the cloud. This is because you need shared storage to create a failover cluster. Shared storage is not available or practical in most public clouds. As a result, Many IT teams kept SQL on-premises. Their experts in IT network, storage, and server would take months to plan, order, install, and configure physical environments for HA failover clustering. Finally, they would spend spent thousands of dollars upgrading to SQL Server Enterprise edition to gain advanced clustering capabilities.
SANless Failover Clustering Enables Cost-Efficient SQL High Availability Protection in the Cloud
Today, SIOS DataKeeper Cluster Edition is the first HA/DR solution to combine fully automated, application-centric clustering and efficient data replication. By integrating seamlessly into Windows Server Failover Clustering (WSFC), it enables a WSFC to work in a cloud where shared storage is not possible. SIOS DataKeeper works by synchronizing local storage in real time using highly efficient block-level replication. In this way, creates a SANless cluster to protect your Windows applications in the cloud. You can use it to protect SQL Server Standard Edition without the need for costly upgrades to SQL Server Enterprise Edition.
Quick Start Templates Make Deploying a Failover Cluster in AWS Easy
Now companies can easily deploy a two-node high-availability failover cluster automatically using an AWS EC2 Quick Start deployment. System administrators and managers can simply purchase the SIOS Amazon Machine Images (AMIs) on AWS Marketplace. They can use the AMI to deploy a two-node SQL Server Standard Edition cluster in the AWS cloud using an AWS Quick Start template.
Quick Start templates are automated reference deployments for key workloads on AWS. Each Quick Start launches, configures and runs the AWS service required to deploy a specific workload on AWS. Importantly, the templates use AWS best practices for security and availability. As a result, Quick Starts eliminate manual steps with a single click – they are fast, low-cost, and customizable.
The SIOS AMIs on AWS Marketplace provide an easy, convenient way for customers to purchase SIOS DataKeeper software to protect business critical applications in AWS. You can use them to deploy a high availability cluster using cost efficient SQL Server Standard Edition in the cloud.
Customers can purchase SIOS DataKeeper through the AWS Marketplace at: https://aws.amazon.com/marketplace/seller-profile?id=3c91e2f7-fc8d-4cce-a8aa-1e37abcb4408
To learn more about the SIOS DataKeeper Quick Start for AWS Cloud, visit: https://aws.amazon.com/quickstart/architecture/sios-datakeeper/
To learn more about the SIOS DataKeeper Cluster Edition for High Availability in Cloud Deployments:
|April 14, 2017||
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.
|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: