The Reliability pillar includes the reliability pillar encompasses the ability of a workload to perform its intended function correctly and consistently when it’s expected to. this includes the ability to operate and test the workload through its total lifecycle. this paper provides in-depth, best practice guidance for implementing reliable workloads on aws.

The reliability pillar provides an overview of design principles, best practices, and questions. You can find prescriptive guidance on implementation in the Reliability Pillar whitepaper.

Design Principles

There are five design principles for reliability in the cloud:


There are four best practice areas for reliability in the cloud:

To achieve reliability you must start with the foundations — an environment where service quotas and network topology accommodate the workload. The workload architecture of the distributed system must be designed to prevent and mitigate failures. The workload must handle changes in demand or requirements, and it must be designed to detect failure and automatically heal itself.

Best Practices


Foundational requirements are those whose scope extends beyond a single workload or project. Before architecting any system, foundational requirements that influence reliability should be in place. For example, you must have sufficient network bandwidth to your data center.

With AWS, most of these foundational requirements are already incorporated or can be addressed as needed. The cloud is designed to be nearly limitless, so it’s the responsibility of AWS to satisfy the requirement for sufficient networking and compute capacity, leaving you free to change resource size and allocations on demand.

The following questions focus on these considerations for reliability.

REL 1: How do you manage service quotas and constraints?
REL 2: How do you plan your network topology?

For cloud-based workload architectures, there are service quotas (which are also referred to as service limits). These quotas exist to prevent accidentally provisioning more resources than you need and to limit request rates on API operations to protect services from abuse. Workloads often exist in multiple environments. You must monitor and manage these quotas for all workload environments. These include multiple cloud environments (both publicly accessible and private) and may include your existing data center infrastructure. Plans must include network considerations, such as intrasystem and intersystem connectivity, public IP address management, private IP address management, and domain name resolution.

Workload Architecture

A reliable workload starts with upfront design decisions for both software and infrastructure. Your architecture choices will impact your workload behavior across all five Well-Architected pillars. For reliability, there are specific patterns you must follow.

With AWS, workload developers have their choice of languages and technologies to use. AWS SDKs take the complexity out of coding by providing language-specific APIs for AWS services. These SDKs, plus the choice of languages, allow developers to implement the reliability best practices listed here. Developers can also read about and learn from how Amazon builds and operates software in The Amazon Builders' Library.

The following questions focus on these considerations for reliability.

REL 3: How do you design your workload service architecture?
REL 4: How do you design interactions in a distributed system to prevent failures?
REL 5: How do you design interactions in a distributed system to mitigate or withstand failures?

Distributed systems rely on communications networks to interconnect components, such as servers or services. Your workload must operate reliably despite data loss or latency in these networks. Components of the distributed system must operate in a way that does not negatively impact other components or the workload.

Change Management

Changes to your workload or its environment must be anticipated and accommodated to achieve reliable operation of the workload. Changes include those imposed on your workload, such as spikes in demand, as well as those from within, such as feature deployments and security patches.

Using AWS, you can monitor the behavior of a workload and automate the response to KPIs. For example, your workload can add additional servers as a workload gains more users. You can control who has permission to make workload changes and audit the history of these changes.

The following questions focus on these considerations for reliability.

REL 6: How do you monitor workload resources?
REL 7: How do you design your workload to adapt to changes in demand?
REL 8: How do you implement change?

When you architect a workload to automatically add and remove resources in response to changes in demand, this not only increases reliability but also ensures that business success doesn't become a burden. With monitoring in place, your team will be automatically alerted when KPIs deviate from expected norms. Automatic logging of changes to your environment allows you to audit and quickly identify actions that might have impacted reliability. Controls on change management ensure that you can enforce the rules that deliver the reliability you need.

Failure Management

In any system of reasonable complexity, it is expected that failures will occur. Reliability requires that your workload be aware of failures as they occur and take action to avoid impact on availability. Workloads must be able to both withstand failures and automatically repair issues.

With AWS, you can take advantage of automation to react to monitoring data. For example, when a particular metric crosses a threshold, you can trigger an automated action to remedy the problem. Also, rather than trying to diagnose and fix a failed resource that is part of your production environment, you can replace it with a new one and carry out the analysis on the failed resource out of band. Since the cloud enables you to stand up temporary versions of a whole system at low cost, you can use automated testing to verify full recovery processes.

The following questions focus on these considerations for reliability.

REL 9: How do you back up data?
REL 10: How do you use fault isolation to protect your workload?
REL 11: How do you design your workload to withstand component failures?
REL 12: How do you test reliability?
REL 13: How do you plan for disaster recovery (DR)?

Regularly back up your data and test your backup files to ensure that you can recover from both logical and physical errors. A key to managing failure is the frequent and automated testing of workloads to cause failure, and then observe how they recover. Do this on a regular schedule and ensure that such testing is also triggered after significant workload changes. Actively track KPIs, such as the recovery time objective (RTO) and recovery point objective (RPO), to assess a workload's resiliency (especially under failure-testing scenarios). Tracking KPIs will help you identify and mitigate single points of failure. The objective is to thoroughly test your workload-recovery processes so that you are confident that you can recover all your data and continue to serve your customers, even in the face of sustained problems. Your recovery processes should be as well exercised as your normal production processes.


Refer to the following resources to learn more about our best practices for Reliability.

Reliability Pillar: AWS Well-Architected
AWS Well-Architected Reliability Labs
The Amazon Builders' Library: How Amazon builds and operates software
AWS Documentation
AWS Global Infrastructure
AWS Auto Scaling: How Scaling Plans Work
Implementing Microservices on AWS
What Is AWS Backup?