Overview of the AWS edge computing toolbox – Getting Started with Edge Computing on AWS

The AWS edge computing strategy aims to provide a comprehensive suite of services and solutions that enable businesses to harness the power of edge computing, addressing the challenges of data processing, latency, security, and scalability. By bringing AWS services and resources closer to end users and devices, this strategy allows organizations to optimize their applications and infrastructure for improved performance, efficiency, and user experience.

Key components of the AWS edge computing strategy include the following topics.

Localized AWS infrastructure and services

AWS offers solutions such as AWS Outposts, AWS Local Zones, and AWS Wavelength to extend the AWS cloud infrastructure to on-premises environments, local data centers, and even mobile networks. These localized infrastructure solutions enable lower latency, reduced data transfer costs, and better compliance with data sovereignty regulations.

AWS Snow Family facilitates edge computing by enabling local data processing, storage, inferences, and analytics without a reliable connection back to the AWS control plane.

Finally, AWS IoT Greengrass can be visualized as a platform for running tiny versions of region-based AWS services you are already familiar with on devices with single-board computers – think Raspberry Pi or Arduino. This includes Amazon SageMaker, AWS Lambda, AWS Kinesis Data Firehose, AWS Kinesis Video Streams, Amazon SNS, AWS Secrets Manager, Amazon CloudWatch, and AWS Systems Manager.

Developer tools and resources

AWS offers a rich ecosystem of developer tools, SDKs, and resources to simplify the development, deployment, and management of edge computing applications. These tools and resources help developers build, test, and monitor applications for edge devices and environments, ensuring seamless integration with AWS cloud services.

Security and compliance

AWS places a strong emphasis on security and compliance, providing robust encryption, access control, and monitoring features for edge computing solutions. This allows organizations to safeguard their data, infrastructure, and applications, while also adhering to industry-specific regulatory requirements.

Consistent experience

The AWS edge computing strategy seamlessly integrates with the broader AWS ecosystem, ensuring that organizations can take advantage of a wide range of AWS services, from compute and storage to analytics and ML, to support their edge computing needs.

Architectural guidance

When architecting a solution, one should always start from a project’s requirements, taking into account situational constraints, risk tolerance, and any assumptions that have been made. It is not possible to architect for all scenarios in which edge computing is useful using a single approach. It solves different problems in different ways depending on the use case.

The AWS Well-Architected Framework addresses patterns and anti-patterns with such an approach in mind. It is an AWS-specific version of more general enterprise architecture frameworks that have existed for decades such as Zachman or TOGAF – if you are familiar with those, you will see their DNA embedded in it.

If you aren’t familiar, don’t worry – just know that the architectural guidance presented both in this book and by the AWS Well-Architected Framework generally is built upon decades of experience in what sort of things need to be considered for the proper function of any system.

Summary

In this chapter, we discussed the opportunities and challenges that edge computing presents. This includes those imposed by the laws of physics, the laws of economics, and the law of the land. We covered the AWS edge computing strategy, which is to extend its infrastructure and services closer to end users, allowing developers and operations staff to have a consistent experience via common APIs and management constructs.

We covered existing IIoT systems and protocols and introduced you to how AWS can integrate with them. We explored how MEC with 5G networks offers a unique opportunity for both MNOs and application developers to extend cloud applications closer to users on mobile devices. We also discussed the challenges associated with DDIL use cases in edge computing, as well as the transformative impact of immersive experiences and given example use cases you may not have considered before.

Finally, we briefly introduced you to AWS Local Zones, AWS Outposts, AWS Snow Family, AWS Wavelength, and AWS IoT Greengrass and placed them in the context of the three primary edge computing use case families.

In the next chapter, we will explore issues associated with networking and security for near-edge computing scenarios.