All posts by Ajay Swamy

Optimize your IoT Services for Scale with IoT Device Simulator

Post Syndicated from Ajay Swamy original https://aws.amazon.com/blogs/architecture/optimize-your-iot-services-for-scale-with-iot-device-simulator/

The IoT (Internet of Things) has accelerated digital transformation for many industries. Companies can now offer smarter home devices, remote patient monitoring, connected and autonomous vehicles, smart consumer devices, and many more products. The enormous volume of data emitted from IoT devices can be used to improve performance, efficiency, and develop new service and business models. This can help you build better relationships with your end consumers. But you’ll need an efficient and affordable way to test your IoT backend services without incurring significant capex by deploying test devices to generate this data.

IoT Device Simulator (IDS) is an AWS Solution that manufacturing companies can use to simulate data, test device integration, and improve the performance of their IoT backend services. The solution enables you to create hundreds of IoT devices with unique attributes and properties. You can simulate data without configuring and managing physical devices.

An intuitive UI to create and manage devices and simulations

IoT Device Simulator comes with an intuitive user interface that enables you to create and manage device types for data simulation. The solution also provides you with a pre-built autonomous car device type to simulate a fleet of connected vehicles. Once you create devices, you can create simulations and generate data (see Figure 1.)

Figure 1. The landing page UI enables you to create devices and simulation

Figure 1. The landing page UI enables you to create devices and simulation

Create devices and simulate data

With IDS, you can create multiple device types with varying properties and data attributes (see Figure 2.) Each device type has a topic where simulation data is sent. The supported data types are object, array, sinusoidal, location, Boolean, integer, float, and more. Refer to this full list of data types. Additionally, you can import device types via a specific JSON format or use the existing automotive demo to pre-populate connected vehicles.

Figure 2. Create multiple device types and their data attributes

Figure 2. Create multiple device types and their data attributes

Create and manage simulations

With IDS, you can create simulations with one device or multiple device types (see Figure 3.) In addition, you can specify the number of devices to simulate for each device type and how often data is generated and sent.

Figure 3. Create simulations for multiple devices

Figure 3. Create simulations for multiple devices

You can then run multiple simulations (see Figure 4) and use the data generated to test your IoT backend services and infrastructure. In addition, you have the flexibility to stop and restart the simulation as needed.

Figure 4. Run and stop multiple simulations

Figure 4. Run and stop multiple simulations

You can view the simulation in real time and observe the data messages flowing through. This way you can ensure that the simulation is working as expected (see Figure 5.) You can stop the simulation or add a new simulation to the mix at any time.

Figure 5. Observe your simulation in real time

Figure 5. Observe your simulation in real time

IoT Device Simulator architecture

Figure 6. IoT Device Simulator architecture

Figure 6. IoT Device Simulator architecture

The AWS CloudFormation template for this solution deploys the following architecture, shown in Figure 6:

  1. Amazon CloudFront serves the web interface content from an Amazon Simple Storage Service (Amazon S3) bucket.
  2. The Amazon S3 bucket hosts the web interface.
  3. Amazon Cognito user pool authenticates the API requests.
  4. An Amazon API Gateway API provides the solution’s API layer.
  5. AWS Lambda serves as the solution’s microservices and routes API requests.
  6. Amazon DynamoDB stores simulation and device type information.
  7. AWS Step Functions include an AWS Lambda simulator function to simulate devices and send messages.
  8. An Amazon S3 bucket stores pre-defined routes that are used for the automotive demo (which is a pre-built example in the solution).
  9. AWS IoT Core serves as the endpoint to which messages are sent.
  10. Amazon Location Service provides the map display showing the location of automotive devices for the automotive demo.

The IoT Device Simulator console is hosted on an Amazon S3 bucket, which is accessed via Amazon CloudFront. It uses Amazon Cognito to manage access. API calls, such as retrieving or manipulating information from the databases or running simulations, are routed through API Gateway. API Gateway calls the microservices, which will call the relevant service.

For example, when creating a new device type, the request is sent to API Gateway, which then routes the request to the microservices Lambda function. Based on the request, the microservices Lambda function recognizes that it is a request to create a device type and saves the device type to DynamoDB.

Running a simulation

When running a simulation, the microservices Lambda starts a Step Functions workflow. First, the request contains information about the simulation to be run, including the unique device type ID. Then, using the unique device type ID, Step Functions retrieves all the necessary information about each device type to run the simulation. Once all the information has been retrieved, the simulator Lambda function is run. The simulator Lambda function uses the device type information, including the message payload template. The Lambda function uses this template to build the message sent to the IoT topic specified for the device type.

When running a custom device type, the simulator generates random information based on the values provided for each attribute. For example, when the automotive simulation is run, the simulation runs a series of calculations to simulate an automobile moving along a series of pre-defined routes. Pre-defined routes are created and stored in an S3 bucket, when the solution is launched. The simulation retrieves the routes at random each time the Lambda function runs. Automotive demo simulations also show a map generated from Amazon Location Service and display the device locations as they move.

The simulator exits once the Lambda function has completed or has reached the fifteen-minute execution limit. It then passes all the necessary information back to the Step Function. Step Functions then enters a choice state and restarts the Lambda function if it has not yet surpassed the duration specified for the simulation. It then passes all the pertinent information back to the Lambda function so that it can resume where it left off. The simulator Lambda function also checks DynamoDB every thirty seconds to see if the user has manually stopped the simulation. If it has, it will end the simulation early. Once the simulation is complete, the Step Function updates the DynamoDB table.

The solution enables you to launch hundreds of devices to test backend infrastructure in an IoT workflow. The solution contains an Import/Export feature to share device types. Exporting a device type generates a JSON file that represents the device type. The JSON file can then be imported to create the same device type automatically. The solution allows the viewing of up to 100 messages while the solution is running. You can also filter the messages by topic and device and see what data each device emits.

Conclusion

IoT Device Simulator is designed to help customers test device integration and IoT backend services more efficiently without incurring capex for physical devices. This solution provides an intuitive web-based graphic user interface (GUI) that enables customers to create and simulate hundreds of connected devices. It is not necessary to configure and manage physical devices or develop time-consuming scripts. Although we’ve illustrated an automotive application in this post, this simulator can be used for many different industries, such as consumer electronics, healthcare equipment, utilities, manufacturing, and more.

Get started with IoT Device Simulator today.

New Amazon Virtual Andon 3.0 – Automate Issue Resolution via APIs and Predictive Services

Post Syndicated from Ajay Swamy original https://aws.amazon.com/blogs/architecture/new-amazon-virtual-andon-3-0-automate-issue-resolution-via-apis-and-predictive-services/

Developing a modern manufacturing enterprise requires careful thought and attention to several priorities. Predictive maintenance and issue resolution automation are likely high on your list. Maximizing your operational efficiency and optimizing output are critical in this competitive global market. As demand grows, manufacturers are under pressure to fulfill increased production needs.

A recent report from McKinsey on Industry 4.0 technologies discusses pandemic implementations such as digital issue detection and resolution. These solutions are critical for crisis response in the COVID-19 era. 30% of manufacturers have highlighted increased operational productivity, reduced time-to-market, and reduction in cost as major strategic imperatives for their Industry 4.0 transformation.

Amazon Virtual Andon 3.0 (AVA) is an Amazon Web Services (AWS) Solution that provides a scalable, digital Andon system to help detect and resolve issues. It optimizes processes, supports your transition to predictive maintenance, and helps prevent future equipment failures.

Overview of new features

AVA provides factory and fulfillment center associates with an intuitive, responsive web interface and workflow. This can be used to raise issues, route those issues to the appropriate engineers, and resolve them in a timely way. With AVA, you can associate explanatory root causes with resolved issues for more insightful reporting. Issue raising and resolution happen digitally. There’s no need for manual intervention (such as raising a manual alarm at a factory workstation.)

AVA introduces the capability to raise issues directly from devices and automated APIs. Flexible integrations can be made directly with your factory devices and systems. Additionally, the APIs integrate with Amazon Machine Learning (ML) services, such as Amazon Lookout for Equipment and Amazon Lookout for Vision. This enables you to automate ML inference into your AVA-based workflows.

With AVA 3.0, you can now monitor your factory floors for disruptions in near-real-time. You can respond and resolve issues quickly, minimizing production disruption. AVA 3.0 provides users with an analytics pipeline so you can create dashboards and custom reports.

Introducing GraphQL APIs

The solution introduces GraphQL APIs via AWS AppSync, secured through AWS Identity and Access Management (IAM) policies. This powers the web interface and functionality to create, route, and manage issues. With AVA GraphQL APIs, you can create and manage site hierarchies, devices, events, and raise and manage issues. For example, you can create an issue by calling the createIssue API to raise issues and track them to completion automatically.

createIssue
{
    id: "<string>",
    siteName: "<string>",
    areaName: "<string>",
    stationName: "<string>",
    deviceName: "<string>",
    processName: "<string>",
    eventId: "<string>",
    eventDescription: "<string>",
    eventType: "<string>",
    issueSource: "<string>",
    priority: "<string>",
    status: "<string>",
    created: "AWSDateTime",
    acknowledged: "AWSDateTime",
    closed: "AWSDateTime",
    acknowledgedTime: "<number>",
    resolutionTime: "<number>",
    createdBy: "<string>",
    additionalDetails: "<string>"
  }

Integration with Amazon Machine Learning services to raise issues automatically

With AVA APIs, you can integrate with predictive services like Amazon Lookout for Equipment (L4E). AVA provides an AWS Lambda function for detecting anomalies generated via L4E. It automatically calls the APIs to create issues for abnormal events.

When L4E detects anomalies in your machinery or production line, AVA can automatically raise issues for those anomalies (Figure 1). This gives you the visibility and tracking mechanism to ensure anomalies are resolved. You can create automated events and issues via APIs by integrating with Amazon Lookout for Equipment. You’re able to track anomalies with their details in near-real-time.

Figure 1. AVA displays anomalies raised from L4E. A prediction score of > 0 indicates that an anomaly has been detected, and AVA will raise an issue with the underlying sensor details.” width=”1063″ height=”611″></p> <p id=Figure 1. AVA displays anomalies raised from L4E. A prediction score of > 0 indicates that an anomaly has been detected, and AVA will raise an issue with the underlying sensor details.

Direct device integration

AVA provides the capability to integrate with IoT devices via AWS IoT Core. You can configure your IoT devices to send data to the ava/issues AWS IoT Core topic. Additionally, AVA can send messages to the ava/devices AWS IoT Core topic to automatically raise issues via MQTT or HTTPS. It maps your machine name to an AVA device and a tag/value combination to an AVA event.

{
  "id": <ID!>,
  "eventId": String,
  "eventDescription": String,
  "type": String,
  "priority": String,
  "siteName": String,
  "processName": String,
  "areaName":" String,
  "stationName": String,
  "deviceName": String,
  "created": AWSDateTime,
  "acknowledged": AWSDateTime,
  "closed": AWSDateTime,
  "status": "open"
}

Analytics pipeline for custom dashboards

AVA uses Amazon DynamoDB to store factory configuration and issues data. All AVA data is exported from the DynamoDB database to an Amazon S3 bucket via an AWS Glue workflow. You can then use Amazon Athena to query underlying data and create custom reports using business intelligence (BI) solutions like Amazon QuickSight (Figure 2). With the analytics pipeline, you can create custom dashboards and monitor your factory operations holistically.

Figure 2. Custom dashboard generated via Amazon QuickSight

Figure 2. Custom dashboard generated via Amazon QuickSight

Architecture and workflow

Figure 3. AVA 3.0 architecture

Figure 3. AVA 3.0 architecture

The AWS CloudFormation template deploys the following infrastructure, shown in Figure 3:

  1. The AWS CloudFormation template provides an Amazon CloudFront web interface that deploys into an Amazon Simple Storage Service (Amazon S3) bucket configured for web hosting.
  2. An Amazon Cognito user pool allows this solution’s administrators to register users and groups using the web interface.
  3. AWS AppSync GraphQL APIs and AWS Amplify power the web interface. Amazon DynamoDB tables store the factory data.
  4. An AWS IoT rule engine helps you monitor manufacturing workstations or devices for events. It then routes the event to the correct engineer for resolution in real time.
  5. Authorized users can interact with and receive notifications from this solution. An AWS Lambda function and Amazon Simple Notification Service (Amazon SNS) send emails and SMS notifications.
  6. Issues created, acknowledged, and closed in the web interface are recorded and updated using AWS AppSync and DynamoDB.
  7. The AWS AppSync GraphQL APIs can be called directly with HTTP POST requests.
  8. If you are using L4E to monitor your machines, enter the name of the Amazon S3 bucket where inference files will be delivered in the Anomaly Detection Output Bucket CloudFormation parameter. This solution can be configured to automatically raise issues if an anomaly is detected.
  9. When the Activate Glue Workflow CloudFormation parameter is set to “Yes”, an AWS Glue workflow will be created to extract data from DynamoDB. It delivers this data via an AWS Glue Data Catalog into Amazon S3. For more information, refer to the Data Analysis section.
  10. You can use an existing SAML provider as an additional identity provider for access to this solution. You can configure the Amazon Cognito Domain Prefix, SAML Provider Name, and SAML Provider Metadata URL CloudFormation parameters. For more information, refer to SAML identity provider.

An intuitive UI with nested events

With AVA, you also can create nested events / issues, so you can more easily get to the root of the problem and resolve issues quickly. The solution builds upon the same intuitive UI as highlighted in this previous Amazon Virtual Andon blog post. In addition, it allows you to manage events granularly, create subevents, raise, and resolve issues and sub issues (Figure 4).

Figure 4. AVA allows you to raise issues and sub issues (for example, ‘Out of boxes’ allows you to specify the kind of boxes you need to resolve the issue)

Figure 4. AVA allows you to raise issues and sub issues (for example, ‘Out of boxes’ allows you to specify the kind of boxes you need to resolve the issue)

Conclusion

Amazon Virtual Andon (AVA) is a self-deployable solution that provides you the digital capability to create, route, manage, and resolve issues. With AVA, you can monitor your overall enterprise for issues and engage with the right engineers to resolve them promptly. It offers a clear, intuitive user interface and straightforward workflow to help team members resolve issues.

Get started with Amazon Virtual Andon today.

Securely Ingest Industrial Data to AWS via Machine to Cloud Solution

Post Syndicated from Ajay Swamy original https://aws.amazon.com/blogs/architecture/securely-ingest-industrial-data-to-aws-via-machine-to-cloud-solution/

As a manufacturing enterprise, maximizing your operational efficiency and optimizing output are critical factors in this competitive global market. However, many manufacturers are unable to frequently collect data, link data together, and generate insights to help them optimize performance. Furthermore, decades of competing standards for connectivity have resulted in the lack of universal protocols to connect underlying equipment and assets.

Machine to Cloud Connectivity Framework (M2C2) is an Amazon Web Services (AWS) Solution that provides the secure ingestion of equipment telemetry data to the AWS Cloud. This allows you to use AWS services to conduct analysis on your equipment data, instead of managing underlying infrastructure operations. The solution allows for robust data ingestion from industrial equipment that use OPC Data Access (OPC DA) and OPC Unified Access (OPC UA) protocols.

Secure, automated configuration and ingestion of industrial data

M2C2 allows manufacturers to ingest their shop floor data into various data destinations in AWS. These include AWS IoT SiteWise, AWS IoT Core, Amazon Kinesis Data Streams, and Amazon Simple Storage Service (S3). The solution is integrated with AWS IoT SiteWise so you can store, organize, and monitor data from your factory equipment at scale. Additionally, the solution provides customers an intuitive user interface to create, configure, monitor, and manage connections.

Automated setup and configuration

Figure 1. Automatically create and configure connections

Figure 1. Automatically create and configure connections

With M2C2, you can connect to your operational technology assets (see Figure 1). The solution automatically creates AWS IoT certificates, keys, and configuration files for AWS IoT Greengrass. This allows you to set up Greengrass to run on your industrial gateway. It also automates the deployment of any Greengrass group configuration changes required by the solution. You can define a connection with the interface, and specify attributes about equipment, tags, protocols, and read frequency for equipment data.

Figure 2. Send data to different destinations in the AWS Cloud

Figure 2. Send data to different destinations in the AWS Cloud

Once the connection details have been specified, you can send data to different destinations in AWS Cloud (see Figure 2). M2C2 provides capability to ingest data from industrial equipment using OPC-DA and OPC-UA protocols. The solution collects the data, and then publishes the data to AWS IoT SiteWise, AWS IoT Core, or Kinesis Data Streams.

Publishing data to AWS IoT SiteWise allows for end-to-end modeling and monitoring of your factory floor assets. When using the default solution configuration, publishing data to Kinesis Data Streams allows for ingesting and storing data in an Amazon S3 bucket. This gives you the capability for custom advanced analytics use cases and reporting.

You can choose to create multiple connections, and specify sites, areas, processes, and machines, by using the setup UI.

Management of connections and messages

Figure 3. Manage your connections

Figure 3. Manage your connections

M2C2 provides a straightforward connections screen (see Figure 3), where production managers can monitor and review the current state of connections. You can start and stop connections, view messages and errors, and gain connectivity across different areas of your factory floor. The Manage connections UI allows you to holistically manage data connectivity from a centralized place. You can then make changes and corrections as needed.

Architecture and workflow

Figure 4. Machine to Cloud Connectivity (M2C2) Framework architecture

Figure 4. Machine to Cloud Connectivity (M2C2) Framework architecture

The AWS CloudFormation template deploys the following infrastructure, shown in Figure 4:

  1. An Amazon CloudFront user interface that deploys into an Amazon S3 bucket configured for web hosting.
  2. An Amazon API Gateway API provides the user interface for client requests.
  3. An Amazon Cognito user pool authenticates the API requests.
  4. AWS Lambda functions power the user interface, in addition to the configuration and deployment mechanism for AWS IoT Greengrass and AWS IoT SiteWise gateway resources. Amazon DynamoDB tables store the connection metadata.
  5. An AWS IoT SiteWise gateway configuration can be used for any OPC UA data sources.
  6. An Amazon Kinesis Data Streams data stream, Amazon Kinesis Data Firehose, and Amazon S3 bucket to store telemetry data.
  7. AWS IoT Greengrass is installed and used on an on-premises industrial gateway to run protocol connector Lambda functions. These connect and read telemetry data from your OPC UA and OPC DA servers.
  8. Lambda functions are deployed onto AWS IoT Greengrass Core software on the industrial gateway. They connect to the servers and send the data to one or more configured destinations.
  9. Lambda functions that collect the telemetry data write to AWS IoT Greengrass stream manager streams. The publisher Lambda functions read from the streams.
  10. Publisher Lambda functions forward the data to the appropriate endpoint.

Data collection

The Machine to Cloud Connectivity solution uses Lambda functions running on Greengrass to connect to your on-premises OPC-DA and OPC-UA industrial devices. When you deploy a connection for an OPC-DA device, the solution configures a connection-specific OPC-DA connector Lambda. When you deploy a connection for an OPC-UA device, the solution uses the AWS IoT SiteWise Greengrass connector to collect the data.

Regardless of protocol, the solution configures a publisher Lambda function, which takes care of sending your streaming data to one or more desired destinations. Stream Manager enables the reading and writing of stream data from multiple sources and to multiple destinations within the Greengrass core. This enables each configured collector to write data to a stream. The publisher reads from that stream and sends the data to your desired AWS resource.

Conclusion

Machine to Cloud Connectivity (M2C2) Framework is a self-deployable solution that provides secure connectivity between your technology (OT) assets and the AWS Cloud. With M2C2, you can send data to AWS IoT Core or AWS IoT SiteWise for analytics and monitoring. You can store your data in an industrial data lake using Kinesis Data Streams and Amazon S3. Get started with Machine to Cloud Connectivity (M2C2) Framework today.

Digitally Optimize your Factory Issue Resolution with Amazon Virtual Andon

Post Syndicated from Ajay Swamy original https://aws.amazon.com/blogs/architecture/digitally-optimize-your-factory-issue-resolution-with-amazon-virtual-andon/

As a manufacturing enterprise, maximizing your operational efficiency and optimizing output are critical in a competitive global market. Global black swan events such as COVID-19 have necessitated the ability to monitor remotely and respond to issues actively, on the factory floor.

Amazon Virtual Andon (AVA) is a digital notification system that helps factory personnel raise, route, track, and resolve factory floor issues more quickly. The solution was incubated in the Amazon Fulfillment Centers to monitor and resolve issues. It is now available as a self-deployable solution that manufacturers can use to optimize productivity.

An intuitive UI to deliver quick value

Amazon Virtual Andon comes with an intuitive user interface (UI) to set up your factory asset hierarchy, so you can model your real factory floor equipment (Figure 1). With AVA, you can create events (incidents) that are likely to occur across your factory. You can granularly tie events to the underlying stations and processes. Factory personnel can monitor manufacturing equipment for events, quickly raise issues, route the problem to specialized engineers, and ensure tracking and resolution of those issues.

Setup and configuration

Figure 1. Create your factory hierarchy

Figure 1. Create your factory hierarchy

With AVA, you can set up your factory configuration quickly (see Figure 2a, 2b following). You can choose to create multiple factory sites, areas, stations, processes, and devices with the setup UI. You can also create users and assign them to Admin, Manager, Engineer, and Associate roles. You can set fine-grained controls on what users can access within the Permissions screen.

Figure 2a. Create new factory areas

Figure 2a. Create new factory areas

Figure 2b. Create factory processes

Figure 2b. Create factory processes

Client screen

In Figure 3, you can see AVA’s graphical client screen, where floor associates can raise issues when breakdowns occur. Associates can raise an issue by clicking on the issue tile. Once issues are raised, they are routed via Amazon Simple Notification Service (SNS) to subscribed emails addresses or phone numbers of specialized engineers for solving those specific breakdown events.

Figure 3. Raise issues screen

Figure 3. Raise issues screen

Observer screen

The issues routed to the engineers are visible on the observer screen, shown in Figure 4. The engineers can confirm that the problem is being addressed. This updates the status on the client screen, and allows for seamless communication between different users on the factory floor. The engineers can then close the issues by specifying a root cause and descriptive text. This enables prescriptive issue resolution, and can assist with future problems.

Figure 4. Issue response screen

Figure 4. Issue response screen

Metrics reporting and API operations

AVA also provides robust reporting seen in Figure 5. This allows managers to view issue metrics for the selected sites and areas in the last seven days. The metric reporting will allow you to observe trends across different areas of your factory floor and optimize accordingly.

Figure 5. Metrics and reporting

Figure 5. Metrics and reporting

Lastly, AVA comes with a rich set of GraphQL APIs that you can use to create sites, issues, and monitor and resolve problems programmatically. In addition, the APIs allow for custom integration with external systems such as factory MES (Manufacturing Execution System) systems, for automated issue creation and solving.

AVA deployment architecture

Deploying this solution with the default parameters builds the following environment in the AWS Cloud:

Figure 6. Amazon Virtual Andon deployment architecture

Figure 6. Amazon Virtual Andon deployment architecture

  1. An Amazon CloudFront web interface deploys into an Amazon Simple Storage Service (S3) bucket configured for web hosting.
  2. An Amazon Cognito user pool activates the solution’s administrators to register users and groups using the web interface.
  3. AWS AppSync GraphQL APIs and AWS Amplify power the web interface. Amazon DynamoDB tables store the factory data.
  4. An AWS IoT rules engine helps users monitor manufacturing workstations or devices for events. It then routes the events to the correct engineer for resolution in real time.
  5. Authorized users can interact with and receive notifications. Using an AWS Lambda function and Amazon SNS, you can send emails and SMS notifications.
  6. Issues created, acknowledged, and closed in the web interface are recorded and updated using AWS AppSync and DynamoDB.

Conclusion

Amazon Virtual Andon (AVA) is a self-deployable solution that helps you resolve factory floor issues more quickly and accurately. With AVA’s intuitive UI and open APIs, you can optimize issue resolution and focus on optimizing production output. You also can holistically monitor your manufacturing site remotely via an intuitive web application. Get started with Amazon Virtual Andon today.

Fast and Cost-Effective Image Manipulation with Serverless Image Handler

Post Syndicated from Ajay Swamy original https://aws.amazon.com/blogs/architecture/fast-and-cost-effective-image-manipulation-with-serverless-image-handler/

As a modern company, you most likely have both a web-based and mobile app platform to provide content to customers who view it on a range of devices. This means you need to store multiple versions of images, depending on the device. The resulting image management can be a headache as it can be expensive and cumbersome to manage.

Serverless Image Handler (SIH) is an AWS Solution Implementation you use to store a single version of every image featured in your content, while dynamically delivering different versions at runtime based on your end user’s device. The solution simplifies code, saves on storage costs, and is ideal for use with web applications and mobile apps. SIH features include the ability to resize images, change background colors, apply formatting, and add watermarks.

Architecture overview

The SIH solution utilizes an AWS CloudFormation template to deploy the solution within minutes, and it’s for those of you who have multiple image assets needing an option to dynamically change or manipulate customer-facing images. SIH deploys best-in-class AWS services such as Amazon CloudFront, Amazon API Gateway, and AWS Lambda functions, and it connects to your Amazon Simple Storage Service (Amazon S3) bucket for storage.

Deploying this solution with the default parameters builds the following environment in AWS Cloud:

SIH: Emvironment in AWS Cloud-2

SIH uses the following AWS services:

  • Amazon CloudFront to quickly and securely  deliver images to your end users at scale
  • AWS Lambda to run code for image manipulation without the need for provisioning or managing servers (thereby reducing costs and overhead)
  • Your Amazon S3 bucket for storage of your image assets
  • AWS Secrets Manager to support the signing of image URLs so that image access is protected

How does Serverless Image Handler work?

When an HTTP request is received from a customer device, it is passed from CloudFront to API Gateway, and then forwarded to the Lambda function for processing. If the image is cached by CloudFront because of an earlier request, CloudFront will return the cached image instead of forwarding the request to the API Gateway. This reduces latency and eliminates the cost of reprocessing the image.

Requests that are not cached are passed to the API Gateway, and the entire request is forwarded to the Lambda function. The Lambda function retrieves the original image from your Amazon S3 bucket and uses Sharp (the open source image processing software) to return a modified version of the image to the API Gateway. SIH also utilizes Thumbor to apply dynamic filters on the fly. Additionally, the solution generates a CloudFront domain name that supports caching in CloudFront. The newly manipulated image is now cached at CloudFront for easy access and retrieval. The end-to-end request and response can be secured by using the solution’s signed URL feature via AWS Secrets Manager, which allows you to prevent unauthorized use of your proprietary images.

Lastly, SIH uses Amazon Rekognition for face detection in images submitted for smart cropping, allowing for easy cropping for specific content and image needs.

Code example of image manipulation

Please refer to the SIH implementation guide to quickly set up and use SIH. Using Node.js, you can create an image request as illustrated below. The code block specifies the image location as myImageBucket and specifies edits of grayscale :true to change the image to grayscale.

const imageRequest = JSON.stringify({
    bucket: “myImageBucket”,
    key: “myImage.jpg”,
    edits: {
        grayscale: true
    }
});

const url = `${CloudFrontUrl}/${Buffer.from(imageRequest).toString(‘base64’)}`;

With the generated URL, SIH can serve the grayscale image.

Conclusion

If you’re looking for a fast and cost-effective solution for image management, Serverless Image Handler provides a great way to manipulate and serve images on the fly with speed and security. Learn more about SIH and watch the accompanying Solving with AWS Solutions video below.