Digital Feedback Loop

Gain real-time analytics and insights from combined OS-level and application-specific data to make data-driven decisions and optimize health, performance, and maintenance of assets deployed at the edge.

Digital feedback loop (DFL) provides rapid feedback on performance of systems and applications operating at the intelligent edge.

Define, embed, and share data including telemetry, logs, images, and events from edge devices at scale. Collect and integrate metadata and telemetry data from deployed systems to provide curated real-time insights to optimize performance, features, and user behavior across these systems. Wind River® Studio closes the feedback loop with a role-based command console to trigger manual or automated responses such as device reboot, power-cycle, configuration update, or switching operating modes.

Wind River Studio Digital Feedback Loop Solutions

DFL Edge Agent (SDK)

Provides a lightweight, platform-agnostic solution to securely connect IoT endpoints during development or operations to a Studio cloud provider of choice. Deployed via the Studio Linux or VxWorks® Build System in the applications and middleware, the DFL Edge Agent enables secure bidirectional connectivity between the device and Studio cloud. Offers both flexibility and simplicity in accessing OS telemetry as well as device-specific data types and custom commands.

Device Management

Provides a scalable framework for the end-to-end management of devices over their lifecycles, from secure enlistment to metadata registration, remotely accessing the device state in real time; and a role-based command console to troubleshoot and manage the devices, both individually and collectively as a fleet.

Real-Time System Analytics

Extends the security hardening of Studio to provide a single pane of glass across the lifecycle of critical embedded workflows. Use early insights from data during development to identify and solve problems before releasing applications. During operation, use machine data to explore customer and device behavior and manage maintenance risks and costs. Configure and auto-send alerts when an anomaly is detected, such as CPU resource utilization exceeding a threshold.

Data Management

Provides built-in support for flexible schemas, a network-efficient communication protocol for data packet management, security for data at rest and in transit, a scalable data pipeline for real-time processing, and REST APIs for integration with analytics and business intelligence tools.

Digital Twin (Powered by Wind River Simics®)

Provides use of DFL in conjunction with a virtual "simulated" machine or system to maintain a persistent record of machine state (irrespective of connectivity) and model machine state through simulation. Explore “what if” scenarios in a virtual environment before working on real hardware – with geographically dispersed teams anywhere in the world.

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Development and Integration

Provides a resource and policy manager for granular role-based access control per device and user group. Allows secure storage of device secrets with provisions for remote renewal and revocation, and RESTful APIs for device interactions with complete traceability including request-response logs.

Digital Feedback Loop

See DFL in action with this robotic test bed demonstration.

In this video, Studio captures data from and passes commands to a robot arm prototype. The demo also dashboards hot path CPU usage of the system. The demonstration was designed and executed by Autumn Chadwick, a senior machine learning software engineer at Wind River.

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How It Works

The Wind River Studio DFL is designed with flexibility and scalability to support use cases in multiple domains for critical infrastructure.

Digital Feedback Loop Use Cases

Digital Feedback Loop use cases

Wind River has partnered with leading cloud service providers Amazon AWS and Microsoft Azure to build on its deep expertise in security and reliability, delivering a solution that scales with the needs of its customers.

Digital Feedback Loop at Cloud Scale

Digital Feedback Loop at cloud scale

DFL provides visibility and actionability that spans timescales from minutes or seconds to years or months. Outliers or anomalies can be detected in real time and addressed or escalated by operations personnel. Data scientists and development teams can gain insights by combining data across a fleet and over time.

Hot, Warm, and Cold Processing Paths

Hot, warm, and cold processing paths

Device enlistment and management

DFL supports multiple approaches for provisioning and securely managing devices, to scale from small numbers of individual, engineered-to-order systems to large volumes of manufactured devices.

Scalability and performance

The DFL edge agent is designed to minimize footprint on resource-constrained edge devices while providing unparalleled visibility into device operation and performance in the field.

Flexibility and configurability

DFL enables developers to choose the fidelity and granularity of data to be shared with an intelligent edge cloud, select types of data to share (metadata, OS or application telemetry, events, complex data types), and specify the commands the device will recognize and respond to.

Digital Feedback Loop Ecosystem Partners

Query, visualize, alert on, and understand your data no matter where it’s stored. With Grafana you can create, explore, and share all of your data through beautiful, flexible dashboards.

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Qlik closes the gaps between data, insights, and action with the only cloud analytics platform built for active intelligence. Make your data and analytics AI-driven, collaborative, and actionable.

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Tableau can help anyone see and understand their data. Connect to DFL telemetry data, drag and drop to create visualizations, and share with a click.

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Wind River Studio Digital Feedback Loop FAQs
What is a digital feedback loop?
Feedback loops occur when information about the effects of actions is used to adapt subsequent actions. Digital feedback loops use digital technology to support that process, often leveraging the speed and reach of digital technologies to collect feedback data (Ramalingam et al., USAID, 2018, “Bridging the Gap: How Real-Time Data Can Contribute to Adaptive Management in International Development”). Microsoft describes a digital feedback loop as the connections among customers, employees, operations, and products as well as the ability to capture, analyze, and act on data from each group.
How do digital feedback loops relate to digital transformation?
Digital feedback loops bring data-centricity to the continuous interactions between employees, customers, operations, and products. These loops have the potential to accelerate digital transformation, generally understood as the evolution of teams, products, and business models as well as the creation of new ones.
Where do edge clouds, edge computing, or edge devices fit into a digital feedback loop?
“Edge” refers both to a location in a network and to a class of scalable compute that takes place at that location. Often this is characterized as compute taking place near where data is created. Edge devices, especially fixed-function embedded devices, have historically generated or logged data that might rarely be looked at or would be sent to the cloud for processing through costly and time-consuming backhaul. Adding data not only from these devices but also from other elements of edge infrastructure (distributed clouds, far edge clouds, storage, etc.) to digital feedback loops opens up new opportunities for insights, especially in the operations and product domains.
Do our runtimes have the ability to send data from the device?
Yes, both VxWorks and Wind River Linux have integrated telemetry capabilities to help make OS data, such as CPU utilization, and application data available. We also support event-driven data, which typically only occurs when a rare incident happens.
What security features have been implemented to ensure that the data at rest and in transit has not been tampered with?
For data in transit, MQTT connections are secured with certificates and mutual TLS, with enhanced certificate management to ensure that only properly authenticated devices can talk to the cloud. Studio security is also used to ensure that only authorized persons can view data and send commands to devices from the cloud. For data at rest, DFL utilizes native encryption from the cloud provider (e.g., AWS).
What is cold path data? Is it supported?
Yes, DFL supports cold path data for longer-term storage and analysis. This data is typically stored in a data vault or data lake provided by a cloud service through a subscription.
Is the data collected analyzed and displayed in a graphical dashboard of some kind? How is it visualized?
Yes, DFL comes with a platform-independent Grafana dashboard for viewing hot path or cold path data over time. The DFL UI also includes views that show and filter collected OS and application data, event data, alerts, and other device information. Sample proofs of concept have been created with third-party ABI tools, such as Qlik, QuickSight, Tableau, and Power BI – these are additional licenses on top of the cloud platform.
Does Studio provide an SDK or API to integrate dashboards and analytics?
Yes, REST APIs are provided to integrate DFL data with custom dashboards and analytics use cases.
Does capturing telemetry data have a dependency on a cloud provider?
Our SDK is designed to be cloud-agnostic while still leveraging essential capabilities of hyperscalers where it makes sense from a cost and scaling perspective. Today we are working to enable our telemetry data with AWS services. Our solution leverages open source technologies and avoids dependencies on the features of a specific cloud provider.
Are customers locked into a cloud provider’s solution?
The DFL edge agent is designed to be highly cloud agnostic, to simplify how developers connect their devices to the cloud and to avoid lock-in. Cloud provider–specific capabilities used (e.g., to maximize performance or scalability) are handled in the edge agent SDK.