Hello readers! Imagine an industrial-level machine that has the ability to create thousands of sensor data points each second. Consider transferring all that data to the cloud without the machine reacting first.
This could cause some latency and cost inefficiencies, along with making the process highly dependent on network connectivity.
And here comes AWS IoT Greengrass, a solution for such challenges.
Unlike the previous solution, where all IoT devices would have to rely on the cloud for processing the data, this new solution brings AWS features close to the data-producing devices. It enables local processing of information, application and marunning applicationsing model running, deployment, and connection to AWS services when needed.
But why do you even need this bridge? How does Greengrass work?
Let's explore it.
What does AWS IoT Greengrass Mean?
Greengrass is an open-source IoT edge runtime and cloud service that enables the development, deployment, and management of applications on IoT devices. Unlike cloud computing, in which all the computations take place in the cloud, Greengrass enables local computation by IoT devices and triggering actionstriggerslocal events.
The significance of this is that IoT systems require lower latency and reliable connectivity.
A temperature sensor installed in a manufacturing plant, for instance, can send each data point to AWS, process it, and then take an action. With Greengrass, the IoT device can perform computation on temperature data locally and take an action without going through the cloud round-trip cycle.
The device can push the necessary information to AWS for various activities such as storage and analysis.
More than Connectivity
You can see Greengrass as just yet another solution that helps you connect IoT devices to AWS.
But you miss its greatest strength.
With Greengrass, your devices get a software environment that allows applications to execute on-premises on the edge. AWS refers to this solution as an extension of cloud processing and intelligence to edge devices and networks with intermittent connections.
This way, you don’t need to pick one over the other for your IoT architecture.
You can have both.
Why Is IoT Greengrass Relevant and Significant?
There is one major reason – data processing location.
Cloud computing is good for centralized data processing, analysis, storage, and applications on a large scale. Nevertheless, there are some IoT workloads that require decision-making right at the point close to the device itself.
Businesses should resort to cloud infrastructure to increase their scope regarding scalability. To learn about the cloud, you can explore the AWS vs Azure for Indian Businesses blog.
Let us consider a scenario where an autonomous machine detects an adverse working condition.
When the machine sends the data to the cloud, processes the data, and then gets the instruction, even the smallest delay might be crucial.
With local processing, the machine will detect the condition and react to it quickly enough.
Nevertheless, it does not mean that all the processes should go to the edge. The benefit comes from determining the place where particular workloads should be processed.
Reduced Latency
If an application processes its data on-site, it does not have to wait until each decision is made in the cloud.
This is why Greengrass can be used in applications requiring quick responses.
Increased Performance In Case of Connectivity Issues
Internet connectivity issues happen to IoT devices from time to time.
This is because an agricultural site, an offshore facility, a manufacturing plant, or even a mobile device can suffer from a poor internet connection.
However, Greengrass lets devices work locally even in such cases.
Reduced Network Traffic for Transmitting Data
Transmitting all raw data to the cloud can sometimes result in unnecessary data transfer.
An alternative is to conduct processing of data by means of an edge application, which then transmits only useful data to AWS.
Thus, a machine providing 100,000 sensor data points per hour might not have to transmit all of them to the cloud.
Rather, the edge application can analyze all the data collected and transmit only those that matter.
This can help reduce the network traffic.
How Does the Greengrass Function?
Understanding its architecture will make it easier for you to comprehend Greengrass.
Essentially, you start off with a computer with IoT Greengrass Core software installed. As Greengrass concentrates on edge workloads, AWS provides serverless computing through services such as AWS Lambda. You can explore more on this topic for your convenience.
This effectively runs local tools and software while connecting to AWS services securely.
It is important to note that you can utilize the AWS cloud for managing your deployments and fleets of devices.
Layer | What Happens? |
Greengrass core device | Processes data and runs applications locally |
IoT devices | Machines and sensors generate data |
Edge runtime | Manages device and deployment software |
AWS Cloud | Offers monitoring, storage, analytics, and other services |
The Greengrass Core Device
The Greengrass core device is an IoT edge device that hosts the edge runtime Core software.
In AWS IoT, the core device is treated as an AWS IoT thing, meaning that you can use AWS IoT for management and organization of the core device.
Imagine the core device as your edge applications’ local computing environment, closer to the IoT device than anything else.
Components
A component is a modular piece of software that you install on Greengrass core devices. It can be any application, library, runtime, or any other software necessary for your edge workload.
It allows more flexibility and gives developers more control over deployments.
For example, one core device might need components for data processing and monitoring, while another needs machine learning inference in addition.
You can effectively achieve this via Greengrass deployments.
What are the Parts of Greengrass Components?
Recipe
The recipe explains how the component operates and includes information about the configuration of the component, dependencies, commands, and platform requirements. Amazon Web Services lets developers develop the recipes in JSON or YAML format.
Artifact
The artifact includes the actual software files required by the component.
They could include scripts, executable programs, or any other file consumed by the ingredient.
How Do Deployments Work?
Imagine that there are 500 industrial devices in multiple locations.
You would not like engineers to connect to each device every time you have some updates to be installed on your software.
Greengrass makes it possible to configure deployments at the level of single devices and device groups. Greengrass sends all necessary configuration and components to targeted devices.
This feature becomes particularly useful in case of managing large numbers of devices.
You can build, test, and publish components and then deploy them to targeted devices. You can manage rollout speed, timeout settings, failure policies, and so on.
What Can IoT Greengrass Do?
Greengrass enables multiple useful edge computing use cases.
Run Applications Locally
Applications can be deployed on the edge device and run there without having to rely on cloud processing all the time.
This is particularly useful in various industrial monitoring environments, smart buildings, agriculture, transport, and other IoT environments.
Run Machine Learning Locally
There are applications that require fast predictions.
Businesses can also extensively use AI in cloud computing. So many organizations are already doing it.
For instance, an application that detects any anomalies in the manufacturing process using machine learning models does not have to upload raw data to the cloud to do inference. It does everything locally and sends meaningful output to AWS.
Process and Filter Data
This is probably one of the most useful things that you can do with Greengrass.
Consider an environment where hundreds of sensors generate a constant flow of readings. You can process those readings locally and upload only important ones to the cloud.
Connect Local Devices
Greengrass can also be used to facilitate communication between local devices.
When Should You Opt for Greengrass?
Greengrass becomes more relevant if there is a clear need for edge computing in your application.
If you have strict response requirements, offline capabilities, local data processing, or central fleet management, Greengrass becomes relevant.
Moreover, using Greengrass is helpful if collecting all raw data in the cloud would result in additional overhead for network traffic.
However, you do not necessarily have to use Greengrass if your architecture includes IoT.
In case you have few devices, a reliable connection, simple flows, and no need for local processing, you could consider other architecture options.
The real question is not ‘Is it possible to use Greengrass?’’
But ‘What should be done locally and what is best to process in the cloud?’
What About Security?
Edge computing poses another critical issue.
By getting your application closer to the hardware, you also have to secure devices, applications, credentials, and local data.
There is also support for security in AWS IoT, including features like device authorization, and authenticatin, secure connections, and encryption of local secrets with AWS services.
Yet, security is a shared responsibility, and AWS advises you to secure your devices, private keys, and local networks, using least privilege and avoiding hardcoding of credentials into Greengrass components.
It is important to consider the possibility of compromising your edge devices and exposing your local applications, connected systems, or data.
Thus, deployment convenience should not be a priority over security.
In this regard, you can check out the AWS security best practices.
Conclusion
IoT Greengrass makes AWS functionality available at the device level while enabling centralized management within the same organization.
It enables devices to do local processing of information, provide fast response to events, work without internet connectivity issues, filter out useless data, perform application and machine learning model executions, and exchange data with other devices.
The component-based architecture of Greengrass also allows developers flexibility in packaging and deploying software on devices.
However, AWS Greengrass doesn’t mean a better IoT architecture.
Its value is only evident when there is a true need for local processing for low-latency, less data transfer, and resilience of operations at the edge.
When this difference is realized, then AWS Greengrass is no longer just another AWS service.
It’s a means to decide where to place your IoT intelligence.