AWS offers a multitude of compute services that support various distributed computing models. These services are quite powerful when used individually, and when used together, they can provide capabilities that support distributed computing strategy for large, complex, or time-critical workloads.

Table of Contents

Edge Processing

One of the key strategies for distributed computing in AWS is edge processing. This involves bringing the processing power closer to the data source, reducing latency and providing real-time (or nearly real-time) data processing.

For example, with edge processing, you could use AWS Wavelength’s ability to distribute your application to edge locations near your end users. By deploying your applications on the AWS edge network, you can provide your end-users with lower latency, enhancing their experience.

AWS Lambda

Another powerful tool for distributed compute strategies is AWS Lambda, a serverless compute service that lets you run your code without provisioning or managing servers. AWS Lambda automatically scales your application in response to incoming request traffic.

A great use case could be processing user profile updates in a social media app. Instead of running servers 24/7 to handle profile updates, you could use Lambda to execute your update functions only when a user updates their profile, saving on computational resources.

Amazon EC2

Amazon Elastic Compute Cloud (Amazon EC2) provides secure, resizable compute capacity in the cloud and is designed to make web-scale cloud computing easier for developers. You can benefit from the control offered by EC2 by using various instance types to handle different workloads within your distributed processing strategy, thus optimizing the cost.

For instance, you could use a smaller EC2 instance type to handle regular traffic and dynamically scale up to larger instances when handling peak traffic loads, thus maintaining performance while optimizing cost.


EC2 Lambda Edge Processing
Management Requires some level of management, including setup and scaling Fully managed, the underlying infrastructure is abstracted Fully managed, the underlying infrastructure is abstracted
Use-cases Suitable for diverse workload types Ideal for event-driven use-cases Optimized for latency-sensitive use-cases
Pricing You pay for the compute instance, whether it’s active or idle You pay only for the compute time you consume Pricing is based on the location of edge nodes

Building Distributed Systems

When designing a distributed system on AWS, make sure to follow best practices such as decoupling your components (using SQS, for example), using different EC2 instance types for different tasks, using managed services like Lambda for event-driven processing, and deploying your workload on AWS edge network for latency-sensitive applications.


Understanding these strategies and how they can be implemented using AWS services such as edge processing, AWS Lambda, and Amazon EC2 is a significant step towards acing the “AWS Certified Solutions Architect – Associate (SAA-C03)” exam. Importantly, these compute strategies also form the building blocks for reliable, scalable, and efficient architectural design in AWS.

Practice Test

True/False: Distributed compute strategies involve the use of a single, centralized server to manage all computational tasks.

  • True
  • False

Answer: False

Explanation: Distributed compute strategies involve spreading computational tasks across multiple computing devices or locations, rather than relying on a single, centralized server.

Which of the following are examples of Distributed compute strategies? (Multiple Select)

  • A. Central Processing
  • B. Edge Processing
  • C. Cloud Computing
  • D. Bulk Data Computing

Answer: B. Edge Processing and C. Cloud Computing

Explanation: Edge processing and cloud computing are forms of distributed computing. In edge processing, data is processed near its source, while cloud computing can distribute computing tasks over the internet.

True/False: In edge computing, data is processed near its source, reducing the need for long-distance communications and speeding up the reaction time.

  • True
  • False

Answer: True

Explanation: This is the underlying principle of edge computing. By processing data near its source, the delay caused by long-distance communications is significantly reduced.

With AWS edge locations, does the data transfer between regional edge caches and the origin server count towards data transfer costs?

  • A. Yes
  • B. No

Answer: B. No

Explanation: Data transfer between regional edge caches and the origin server do not incur any data transfer costs with AWS Edge Locations.

Which of the following services can be used for edge computing in AWS? (Multiple select)

  • A. AWS Greengrass
  • B. AWS Glacier
  • C. AWS Snowball Edge
  • D. Amazon S3

Answer: A. AWS Greengrass, C. AWS Snowball Edge

Explanation: AWS Greengrass and AWS Snowball Edge are specifically designed for edge computing. They allow you to run local compute, messaging, data caching, and sync capabilities for connected devices in a secure way.

True/False: Edge locations in AWS have lesser durability compared to regional data centers.

  • True
  • False

Answer: False

Explanation: Edge locations are highly available and redundant. They deliver services like Amazon Cloudfront and AWS Shield with high availability and performance.

In AWS, how many edge locations are typically present in a region?

  • A. 1
  • B. 2
  • C. 4
  • D. It varies by region

Answer: D. It varies by region

Explanation: The number of edge locations can vary by region depending on the demand and requirements of that particular region.

True/False: Distributed computing strategies can help reduce latency and enhance user experience.

  • True
  • False

Answer: True

Explanation: By processing data near the source or across multiple servers, latency can be reduced and the user experience can be dramatically improved.

What is the FaaS (Function as a Service) platform of AWS, which is often employed in distributed compute strategies?

  • A. AWS Lambda
  • B. AWS Elastic Beanstalk
  • C. AWS Batch
  • D. AWS Fargate

Answer: A. AWS Lambda

Explanation: AWS Lambda lets you run code without provisioning or managing servers, which makes it an important tool for distributed compute strategies.

True/False: AWS Snowball Edge is a data migration and edge computing device with onboard storage and compute capabilities.

  • True
  • False

Answer: True

Explanation: AWS Snowball Edge is a physically transportable device designed for large-scale data transfer and edge computing workloads, with onboard storage and compute capabilities.

Edge computing is more suitable for environments where:

  • A. Low internet connectivity is a challenge
  • B. High performance is not necessary
  • C. Neither A nor B
  • D. Both A and B

Answer: A. Low internet connectivity is a challenge

Explanation: In environments where low internet connectivity is a challenge, edge computing can replicate cloud services locally, reducing latency and enhancing performance.

True/False: AWS Greengrass allows you to process data streams locally with automatic synchronization to AWS.

  • True
  • False

Answer: True

Explanation: AWS Greengrass can process data locally and synchronize with AWS when connectivity is available. This enables you to make local data-driven decisions.

Which of the following are benefits of Distributed Compute Strategies? (Multiple select)

  • A. Improved Latency
  • B. Increased Cost
  • C. Better Scalability
  • D. More Centralization

Answer: A. Improved Latency and C. Better Scalability

Explanation: Distributed compute strategies typically lead to improved latency and better scalability. They allow for high availability and fault tolerance without the need for centralized servers.

True/False: Lambda@Edge lets you run Node.js and Python lambda functions to customize content that CloudFront delivers.

  • True
  • False

Answer: True

Explanation: Lambda@Edge is a feature of AWS CloudFront that lets you run code closer to users of your application, which improves performance and reduces latency.

What is the primary storage service used for big data analytics and distributed compute services by Amazon?

  • A. Amazon DynamoDB
  • B. Amazon S3
  • C. Amazon EFS
  • D. Amazon Glacier

Answer: B. Amazon S3

Explanation: Amazon S3 (Simple Storage Service) is an object storage service that offers industry-leading scalability, data availability, security, and performance.

Interview Questions

What is edge processing in the context of Distributed Compute Strategies?

Edge processing is a distributing computing framework that places computing resources, such as computation and data storage, closer to the data source or client. This strategy reduces latency and bandwidth use by processing data at the edge of the network, away from the centralized computing resources.

In which AWS service would you typically implement edge processing?

AWS provides a variety of services for edge computing, including AWS Wavelength, AWS Snow Family (Snowball, Snowmobile), AWS Outposts, AWS Greengrass and AWS Lambda@Edge.

What is the core benefit of a distributed compute strategy?

The core benefit of a distributed compute strategy is its ability to process and manage data across a network of interconnected computers or servers. This strategy allows for greater scalability, data redundancy, and reliability, as well as improved fault tolerance and low latency.

What is the primary reason for implementing edge processing in an AWS architecture?

The primary reason to implement edge processing in an AWS architecture is to reduce latency. By processing data closer to the source, organizations can respond to data changes more quickly, making applications more responsive.

What AWS services can be used to reduce latency by processing data closer to the edge of the network?

AWS offers services like AWS Wavelength and AWS Lambda@Edge that allow you to process data closer to the edge of the network and, subsequently, reduce latency.

What is AWS Lambda@Edge?

AWS Lambda@Edge is a feature of Amazon CloudFront that allows you to run code closer to users of your application, which improves performance and reduces latency.

What use cases are most suitable for AWS Outposts?

AWS Outposts is most suitable for use cases where applications need to run on premises due to low latency requirements or local data processing needs. This includes applications such as manufacturing process automation, health care systems, video streaming services, and more.

How does AWS Snowball assist with edge processing?

AWS Snowball is a data transport solution that accelerates moving large amounts of data into and out of AWS using portable storage devices for transport. This can help with edge processing as it may be used to bring large datasets closer to the compute resources to speed up the processing time.

What AWS service allows you to bring AWS services to your on-premises workloads?

AWS Outposts allows you to bring AWS services, infrastructure, and operating models to virtually any data center, co-location space, or on-premises facility.

In the context of AWS, what is a Snowball Edge?

AWS Snowball Edge is a type of AWS Snowball device that provides onboard storage and compute capabilities. These devices can undertake a subset of local processing and are useful in situations where connectivity may be limited or inconsistent.

How does AWS Wavelength enable edge computing?

AWS Wavelength brings AWS services to the edge of the 5G network, minimizing the latency to connect to an application from a mobile device. It allows developers to build applications that deliver single-digit millisecond latencies to mobile devices and end users.

What is the purpose of Amazon CloudFront in Edge Computing?

Amazon CloudFront is a content delivery network (CDN) that delivers data, videos, applications, and APIs to customers globally with low latency and high transfer speeds. In the context of Edge Computing, CloudFront works by caching content at the edge locations to serve it to the users from the nearest point, therefore minimizing latency.

What functions does AWS Greengrass provide in edge computing?

AWS Greengrass is a service that extends AWS to edge devices so they can act locally on the data they generate, while still using the cloud for management, analytics, and durable storage. It allows IoT devices to collect and analyze data closer to the source of information, react autonomously to local events, and communicate with other devices on local networks.

How is latency minimized in AWS Lambda@Edge?

AWS Lambda@Edge minimizes latency by allowing developers to execute functions closer to the user’s geographical location via a global network of edge locations. When an event triggers a function, it can be processed at the edge location, reducing the round-trip time.

What is the main advantage of using AWS Snowball edge in data migration?

The main advantage of using AWS Snowball Edge for data migration is its ability to undertake large scale data transfers. Physically transporting data using Snowball Edge can be faster, more cost effective, and more secure than transferring that same data over the internet.

Leave a Reply

Your email address will not be published. Required fields are marked *