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Fuel Your Business with an Intelligent Edge

We are excited to announce the general availability of Xi IoT to the public. It’s a simple, scalable, and secure intelligent edge platform for real-time analysis delivered as a part of Xi Cloud Services. The core essence of Nutanix solutions is to provide simplicity in all aspects of the entire IT stack from the core datacenter and all the way to the edge. Xi Cloud Services allows you build and operate a hybrid cloud with both Software-as-a-Service (SaaS) and cloud services with pay-as-you-grow consumption models. Easily enable services on-demand with one-click operations, and focus the enterprise to drive increased revenues and better overall end user experiences.

While we strive to deliver a great hybrid cloud experience, our large enterprise customers have driven us to explore unsolved problems at the edge. The challenge with the edge has a lot to do with the proliferation of the IoT (Internet of Things) including devices and sensors. In 2017, 3 billion enterprise IoT edge devices generated over 32 times more data (256 ZB) than 30+ million nodes across cloud and private data centers.1 The classic IoT model of ingesting massive amounts of data on edge devices and processing data in the cloud, leads to several challenges:

  • Latency and scale – processing data remotely wouldn’t be “real-time”
  • Lack of autonomy – network drops due to satellite connectivity (i.e. oil rigs)
  • Security – sensitive data traveling unencrypted over the network
  • Compliance – compliance that may require data to remain “in country”


Figure: Limited Bandwidth at the Edge2

1 Barnett, Thomas, et al. “Cisco Global Cloud Index 2015–2020.” Cisco Global Cloud Index 2015–2020, Cisco, Nov. 2016, www.cisco.com/c/dam/m/en_us/service-provider/ciscoknowledgenetwork/files/622_11_15-16-Cisco_GCI_CKN_2015-2020_AMER_EMEAR_NOV2016.pdf.
2 Barnett, Thomas, et al. “Cisco Global Cloud Index 2015–2020.” Cisco Global Cloud Index 2015–2020, Cisco, Nov. 2016, www.cisco.com/c/dam/m/en_us/service-provider/ciscoknowledgenetwork/files/622_11_15-16-Cisco_GCI_CKN_2015-2020_AMER_EMEAR_NOV2016.pdf.

With the right edge computing and IoT platform, deploying planet-scale edge intelligence is straightforward, cost-effective, and a path to unprecedented innovation within the enterprise. A “best-practices” IoT platform approach will enable rapid application development instead of one-off application silos with questionable security. It must also be a simple stack for operators to set up, deploy, scale, monitor, and support without specialized training or skills.

Introduction to the Xi IoT Platform

The Xi IoT platform is a 100% software-defined solution that delivers local compute, machine learning and intelligence for your IoT edge devices, converging the edge and your choice of cloud into one seamless, delightful application development platform. Xi IoT eliminates complexity, accelerates deployment, and elevates developers to focus on the business logic powering IoT applications and services. The Xi IoT platform consists of Xi Edge, a Data Pipeline and a SaaS Infrastructure with application management. Xi Edge is the computing stack for real-time processing of sensor or device data at the edge. The Data Pipeline enables seamless connectivity between the edge and your choice of clouds. And you can easily manage the entire data and application lifecycle through an easy to use SaaS Infrastructure.


Figure: Xi IoT and Xi Edge, Powering the Intelligent Edge

There are several key advantages to the Xi IoT platform, versus traditional models.

  1. Shift data processing from the cloud to edge for real-time processing
  2. Platform approach versus vertical approach
  3. No API lock-in
  4. Simplicity with zero-touch setup and operations
  5. Security throughout the platform
  6. Extensibility of the platform
  7. Freedom to choose any cloud
  1. Xi IoT is built for latency-sensitive microservices to enhance end user experience. Imagine a scenario where it takes data hundreds of milliseconds to travel from the edge to cloud and back and how that impacts the business. Instead, with Xi Edge, enterprises can easily shift the processing from the cloud to the edge to reduce that latency to a few milliseconds. This not only increases local response times, it may reduce overall bandwidth costs. Consider a manufacturing scenario with robots or automated machinery being able to proactively detect if they were to overheat or burn out based on sensor data collected over long periods of time. This information could save the company from unexpected downtime.


    Figure: Real-time Edge Benefits with Xi IoT

  2. Many IoT applications today are developed as point solutions because a platform that can ingest all sorts of sensor or device data doesn’t truly exist. This creates unnecessary complexity with security and issues with scaling. Xi IoT works differently, providing a platform approach versus a vertical approach for developing applications,
  3. The Xi IoT platform is open for developers to quickly build and deploy new containerized applications and functions without being locked-in to specific public cloud APIs. Developers can also easily bring their own Machine Learning (ML) models to the edge to quickly analyze IoT data in real-time. As more and more complex applications are developed in containers versus virtual machines, developers will find the Xi IoT platform easily integrates with docker registries to drop into existing CI/CD workflows without disruption.

  4. The Xi IoT platform is open for developers to quickly build and deploy new containerized applications and functions without being locked-in to specific public cloud APIs. Developers can also easily bring their own Machine Learning (ML) models to the edge to quickly analyze IoT data in real-time. As more and more complex applications are developed in containers versus virtual machines, developers will find the Xi IoT platform easily integrates with docker registries to drop into existing CI/CD workflows without disruption.
  5. The classic model for setting up and deploying edge infrastructure requires specialized skills, however, Xi IoT was designed with the remote edge in mind. It’s built with minimal edge expertise required. Depending on how the edge is deployed, as a virtual machine or a bare-metal server, all that is required is basic Internet connectivity and it will pull down the configuration from a SaaS management plane. This drastically reduces the time required to get the edge configured and functional. The SaaS management plane then allows you quickly configure data pipelines from multiple sources, process them at the edge, and send to any cloud.


    Figure: Make Edge Invisible

  6. Security has to be top-notch as data flowing from sensors and devices to the edge, and from edge to cloud must be over encrypted tunnels. Additionally, analyzing data at the edge and only sending filtered data to the cloud limits the attack surface. In order to further reduce the attack surface, human access to edge devices is limited. Beyond that, the platform is designed to limit access to authorized users based on role.
  7. The Xi IoT platform function service has many built-in programming language choices available to IoT application developers like Python, Node JS and Go, and developers can easily extend the platform with their preferred language. They can bring their own ML models and their preferred Artificial Intelligence (AI) inferencing to the edge. It’s a single platform that runs containers and functions as a service.
  8. Traditional IoT models offered by public cloud providers have some inherent limitations. They are designed to backhaul IoT data from the edge to their respective cloud. This significantly limits the choice for enterprises that may or may not want to send this data to a public cloud.
  9. It doesn’t always work, and if it does, it needs to be scrubbed before being sent. The Xi IoT platform removes the strain from writing connectors to simply creating a data pipeline and selecting the cloud of your choice (Microsoft Azure, Amazon Web Services, Google Cloud Platform), followed by the appropriate service. For example, the data pipeline can route to AWS S3 for storage purposes or AWS Kinesis for analysis, it’s not locked-in to a particular cloud or service.


Figure: Analyze Data at the Edge

Use Cases

Xi IoT can provide benefits across verticals and drive new revenue streams.

Manufacturing:
Increase efficiency and maximize productivity by using edge intelligence to predict equipment failure, detect process anomalies, improve quality control, and manage energy consumption. Real-time analysis reduces decision latency and minimizes costly production delays.

Retail:
Deliver unique customer experiences by leveraging data at the edge to personalize offers, build an omnichannel customer relationship, and streamline the purchase process. Edge data can also improve inventory management, ensuring product availability and easing supply chain strains.

Oil and Gas:
Transform upstream and downstream operations with edge intelligence. Real-time analysis of well sites can optimize extraction processes, and analysis at retail locations can identify trends to maximize revenue.

Smart Cities:
Connected city services can dynamically improve traffic flow when trouble spots appear, dispatch emergency personnel quickly, and detect issues with utilities before they become problems. With the amount of data involved from all devices and sensors across the city, computing at the edge is the only viable approach.

Healthcare:
Edge-based diagnostic equipment and monitoring tools bring processing and analysis closer to the patient, improving patient services while limiting data transfer in an effort to help with greater patient privacy. A health care providers ability to access real-time data for assessment and diagnosis could make a significant impact on patient outcomes.

Here is an example of how data from the edge can be analyzed from multiple sources. Take a look at the image below; sensor and device data flowing from an imaging device is sent to Xi IoT, real-time analysis is performed, and commands are sent to the PLC (Programmable Logic Controller) to remove objects which don’t meet compliance with a robotic arm. This drastically reduces human error in the manufacturing process.


Figure: Product Quality Check

Edge analytics apply to verticals in a variety of ways, including:

As your organization looks to embark on the journey from the cloud to the edge, consider a platform approach that enables many use cases and doesn’t lock you in to point solutions. The journey shouldn’t be difficult—and we are here to ensure that.

Take a look at www.nutanix.com/iot to learn more, or request a demo at https://www.nutanix.com/products/iot/#demo. Looking forward to disrupting the edge together!

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