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AI and predictive analytics are essential features in modern storage systems, helping to assess the health of the infrastructure and identify problems and storage automation capabilities to keep these systems running smoothly.

Gartner recently identified augmented data management as No. 2 on its list of 10 top data and analytics trends for 2020. Vendors are adding machine learning and AI capabilities to provide self-configuring and self-tuning data management to free IT staff to focus on higher-value tasks, according to the research firm.

AI and predictive analytics are part of that trend, however, there's been a good bit of hype and exaggeration around the use of those technologies. At times, vendors apply the AI label in vague and inaccurate ways. Nevertheless, predictive analytics tools can deliver tangible benefits in terms of storage automation performance, efficiency and lower operating costs.

What follows are answers to six key questions about AI, predictive analytics and storage automation.

Predictive analytics uses technologies such as data mining, analytical queries, predictive modeling, AI and machine learning to uncover patterns in storage system data to identify anomalies, forecast future outcomes and fix problems using storage automation capabilities. Predictive analytics capabilities in storage products offer several advantages: better performance and resource use, lower administrative overhead, reduced downtime and optimized workloads.

Predictive analytics capabilities in storage products offer several advantages: better performance and resource use, lower administrative overhead, reduced downtime and optimized workloads.

Machine learning is particularly important to improve predictive analytics in the latest storage systems. Data collected from across the storage infrastructure is used to train the predictive algorithms to understand trends and recognize when bottlenecks and problems might arise. Storage components can then be continuously optimized and potential issues addressed.

Increasing use of all-flash arrays and hybrid and hyper-converged infrastructure are among the factors driving demand for smarter storage. Storage doesn't exist in a vacuum anymore. In many enterprises, it's an integrated part of the data center technology stack that's managed as a whole and requires more real-time data on storage capacity and performance that intelligent storage provides.

Cloud storage incorporates predictive analytics in ways that help forecast trends, plan infrastructures and reduce overhead. These applications use AI, machine learning and other advanced techniques to continuously collect and analyze telemetry data from flash arrays in the storage stack. Here, too, the goal is to analyze IOPS, bandwidth, fault tolerance, latency and other storage-related information, identify problems and use storage automation capabilities to proactively address them.

"Cloud-based predictive analytics platforms go far beyond the remote monitoring systems of a prior generation," said Eric Burgener, a research vice president at IT analyst firm IDC, in a whitepaper. He identified three key features that differentiate cloud-based predictive analytics from traditional remote monitoring: data sharing, scope of monitoring, and use of AI and machine learning to drive autonomous operations.

In the cloud context, predictive analytics takes storage infrastructure one step closer to a self-healing technology that identifies and resolves issues with minimal intervention.

Data collection is part of the foundation of smart storage. Vendors aggregate and analyze anonymized telemetry data from their customers to learn about different storage scenarios, workloads or problems that could affect other customers. Vendors can provide detailed information on storage performance metrics and patterns, as well as forecast trends, bottlenecks and other potential issues before they occur.

Predictive analytics works in tandem with machine learning and deep learning to make these systems work without preprogrammed instructions.

When vetting storage systems, look for these five key predictive analytics features to ensure you get the capabilities you'll need:

AI-driven analytics software -- and its predictive and prescriptive functionalities -- comes standard in most storage products from major vendors. IT buyers should compare AI-enhanced storage products from leading vendors such as Dell EMC, Hewlett Packard Enterprise, Hitachi Vantara, IBM, Infinidat, NetApp and Pure Storage, along with smaller vendors and startups.

Originally posted here:
6 basics of storage automation, predictive analytics and AI - TechTarget

Amazon founder Jeff Bezos has long talked about moving manufacturing and other industrial activities off the earth as a way to preserve our only home. The cloud giant took one small step toward a sustainable commercial space economy this week with the launch of a dedicated space business unit.

Amazon Web Services Aerospace and Satellite Solutions operation is built around ground stations that provide downlinks to satellites, then process, store and transmit image and other sensor data they capture. The goal, the company said Tuesday (June 30), is to bring AWS services and solutions to the space enterprise.

The service would offer low-latency Internet access, high-resolution Earth observation and ubiquitous Internet of Things connectivity. The effort includes launching new services that process space data on Earth and in orbit, AWS said in a blog post unveiling the orbiting cloud business.

The company (NASDAQ: AMZN) also announced the hiring of retired U.S. Air Force Major General Clint Crosier to head the new space business unit. Crosier previously served as planning director with the U.S. Space Force.

Among the first components of the space cloud effort is the AWS Ground Station, a managed satellite communications service used to downlink, process and distribute satellite data in near real-time. It can also be used to uplink satellite commands across multiple cloud regions. Among the early customers for the service is NASAs Jet Propulsion Laboratory and aerospace giant Lockheed Martin Corp. (NYSE: LMT).

Another early ground station customer, earth observation specialist Capella Space, uses AWS infrastructure to operate a constellation of synthetic-aperture radar satellites. The San Francisco-based company plans to launch hourly SAR global coverage that could be delivered to commercial and government users in near-real time.

Using web applications or APIs, customers request coverage over specific locations. Those imaging tasks are routed to Capella satellites. Imagery is then downlinked to the AWS cloud for processing and delivery.

The AWS space network would link cloud customers to data lakes and storage, edge computing, virtual mission operations, secure satellite connectivity, image processing and intelligence analytics as well as AI and machine learning capabilities, the company said.

The managed service is positioned as a way to communicate with satellites, process data and scale those operations once data are migrated to the cloud. Ground stations are located in the AWS U.S. West (Oregon), U.S. East (Ohio), European Union (Stockholm, Sweden), and Middle East (Bahrain). An Asia Pacific ground station came online in Sydney, Australia, in April, the companys first in the Southern Hemisphere.

The network streams satellite data to the AWS Elastic Compute Cloud for processing. Data sets are then stored and archived. Among the data analytics options are the companys Rekognition image analyzer along with SageMaker for applying AI and machine learning algorithms to satellite imagery.

Ultimately, satellite communications and data transfer rely on access to ground-based antennas. Hence, AWS emphasized that ground station users pay only for the actual antenna time that [they] use. The pay-as-you-go model allows customers to use any antenna in the AWS ground station network.

Those data are then downlinked to the appropriate AWS cloud region to speed processing. Immediate data processing is promoted for satellite applications such as weather forecasting or responding to natural disasters.

That consumption pricing model also addresses growing concerns about skyrocketing data egress charges as data are move in and out of cloud storage.

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About the author: George Leopold

George Leopold has written about science and technology for more than 30 years, focusing on electronics and aerospace technology. He previously served as executive editor of Electronic Engineering Times. Leopold is the author of "Calculated Risk: The Supersonic Life and Times of Gus Grissom" (Purdue University Press, 2016).

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AWS Takes the High Ground with Space Unit - EnterpriseAI