Reference

Design Principle

The following are design principles for operational excellence in the cloud:

• Perform operations as code: In the cloud, you can apply the same engineering discipline that you use for application code to your entire environment. You can define your entire workload (applications, infrastructure, etc.) as code and update it with code. You can script your operations procedures and automate their process by launching them in response to events. By performing operations as code, you limit human error and create consistent responses to events.

• Make frequent, small, reversible changes: Design workloads that are scalable and loosely coupled to permit components to be updated regularly. Automated deployment techniques together with smaller, incremental changes reduces the blast radius and allows for faster reversal when failures occur. This increases confidence to deliver beneficial changes to your workload while maintaining quality and adapting quickly to changes in market conditions.

• Refine operations procedures frequently: As you evolve your workloads, evolve your operations appropriately. As you use operations procedures, look for opportunities to improve them. Hold regular reviews and validate that all procedures are effective and that teams are familiar with them. Where gaps are identified, update procedures accordingly. Communicate procedural updates to all stakeholders and teams. Gamify your operations to share best practices and educate teams.

• Anticipate failure: Perform “pre-mortem” exercises to identify potential sources of failure so that they can be removed or mitigated. Test your failure scenarios and validate your understanding of their impact. Test your response procedures to ensure they are effective and that teams are familiar with their process. Set up regular game days to test workload and team responses to simulated events.

• Learn from all operational failures: Drive improvement through lessons learned from all operational events and failures. Share what is learned across teams and through the entire organization.

• Use managed services: Reduce operational burden by using AWS managed services where possible. Build operational procedures around interactions with those services.

• Implement observability for actionable insights: Gain a comprehensive understanding of workload behavior, performance, reliability, cost, and health. Establish key performance indicators (KPIs) and leverage observability telemetry to make informed decisions and take prompt action when business outcomes are at risk. Proactively improve performance, reliability, and cost based on actionable observability data.

Best Practices

• Organization
• Prepare
• Operate
• Evolve

Design Principle

• Implement a strong identity foundation: Implement the principle of least privilege and enforce separation of duties with appropriate authorization for each interaction with your AWS resources. Centralize identity management, and aim to eliminate reliance on long-term static credentials.

• Maintain traceability: Monitor, alert, and audit actions and changes to your environment in real time. Integrate log and metric collection with systems to automatically investigate and take action.

• Apply security at all layers: Apply a defense in depth approach with multiple security controls. Apply to all layers (for example, edge of network, VPC, load balancing, every instance and compute service, operating system, application, and code).

• Automate security best practices: Automated software-based security mechanisms improve your ability to securely scale more rapidly and cost-effectively. Create secure architectures, including the implementation of controls that are defined and managed as code in version-controlled templates.

• Protect data in transit and at rest: Classify your data into sensitivity levels and use mechanisms, such as encryption, tokenization, and access control where appropriate.

• Keep people away from data: Use mechanisms and tools to reduce or eliminate the need for direct access or manual processing of data. This reduces the risk of mishandling or modification and human error when handling sensitive data.

• Prepare for security events: Prepare for an incident by having incident management and investigation policy and processes that align to your organizational requirements. Run incident response simulations and use tools with automation to increase your speed for detection, investigation, and recovery.

Best Practices

• Security
• Identity and access management
• Detection
• Infrastructure protection
• Data protection
• Incident response
• Application security

Design Principle

There are five design principles for reliability in the cloud:

• Automatically recover from failure: By monitoring a workload for key performance indicators (KPIs), you can start automation when a threshold is breached. These KPIs should be a measure of business value, not of the technical aspects of the operation of the service. This provides for automatic notification and tracking of failures, and for automated recovery processes that work around or repair the failure. With more sophisticated automation, it’s possible to anticipate and remediate failures before they occur.

• Test recovery procedures: In an on-premises environment, testing is often conducted to prove that the workload works in a particular scenario. Testing is not typically used to validate recovery strategies. In the cloud, you can test how your workload fails, and you can validate your recovery procedures. You can use automation to simulate different failures or to recreate scenarios that led to failures before. This approach exposes failure pathways that you can test and fix before a real failure scenario occurs, thus reducing risk.

• Scale horizontally to increase aggregate workload availability: Replace one large resource with multiple small resources to reduce the impact of a single failure on the overall workload. Distribute requests across multiple, smaller resources to verify that they don’t share a common point of failure.

• Stop guessing capacity: A common cause of failure in on-premises workloads is resource saturation, when the demands placed on a workload exceed the capacity of that workload (this is often the objective of denial of service attacks). In the cloud, you can monitor demand and workload utilization, and automate the addition or removal of resources to maintain the more efficient level to satisfy demand without over- or under-provisioning. There are still limits, but some quotas can be controlled and others can be managed (see Manage Service Quotas and Constraints).

• Manage change in automation: Changes to your infrastructure should be made using automation. The changes that must be managed include changes to the automation, which then can be tracked and reviewed.

Best Practices

• Foundations
• Workload architecture
• Change management
• Failure management

Design Principle

• Implement Cloud Financial Management: To achieve financial success and accelerate business value realization in the cloud, invest in Cloud Financial Management and Cost Optimization. Your organization should dedicate time and resources to build capability in this new domain of technology and usage management. Similar to your Security or Operational Excellence capability, you need to build capability through knowledge building, programs, resources, and processes to become a cost-efficient organization.

• Adopt a consumption model: Pay only for the computing resources that you require and increase or decrease usage depending on business requirements, not by using elaborate forecasting. For example, development and test environments are typically only used for eight hours a day during the work week. You can stop these resources when they are not in use for a potential cost savings of 75% (40 hours versus 168 hours).

• Measure overall efficiency: Measure the business output of the workload and the costs associated with delivering it. Use this measure to know the gains you make from increasing output and reducing costs.

• Stop spending money on undifferentiated heavy lifting: AWS does the heavy lifting of data center operations like racking, stacking, and powering servers. It also removes the operational burden of managing operating systems and applications with managed services. This permits you to focus on your customers and business projects rather than on IT infrastructure.

• Analyze and attribute expenditure: The cloud makes it simple to accurately identify the usage and cost of systems, which then permits transparent attribution of IT costs to individual workload owners. This helps measure return on investment (ROI) and gives workload owners an opportunity to optimize their resources and reduce costs.

Best Practices

• Practice Cloud Financial Management
• Expenditure and usage awareness
• Cost-effective resources
• Manage demand and supply resources
• Optimize over time

Design Principle

• Democratize advanced technologies: Make advanced technology implementation smoother for your team by delegating complex tasks to your cloud vendor. Rather than asking your IT team to learn about hosting and running a new technology, consider consuming the technology as a service. For example, NoSQL databases, media transcoding, and machine learning are all technologies that require specialized expertise. In the cloud, these technologies become services that your team can consume, permitting your team to focus on product development rather than resource provisioning and management.

• Go global in minutes: Deploying your workload in multiple AWS Regions around the world permits you to provide lower latency and a better experience for your customers at minimal cost.

• Use serverless architectures: Serverless architectures remove the need for you to run and maintain physical servers for traditional compute activities. For example, serverless storage services can act as static websites (removing the need for web servers) and event services can host code. This removes the operational burden of managing physical servers, and can lower transactional costs because managed services operate at cloud scale.

• Experiment more often: With virtual and automatable resources, you can quickly carry out comparative testing using different types of instances, storage, or configurations.

• Consider mechanical sympathy: Understand how cloud services are consumed and always use the technology approach that aligns with your workload goals. For example, consider data access patterns when you select database or storage approaches.

Best Practices

• Architecture selection
• Compute and hardware
• Data management
• Networking and content delivery
• Process and culture

Design Principle

• Understand your impact: Measure the impact of your cloud workload and model the future impact of your workload. Include all sources of impact, including impacts resulting from customer use of your products, and impacts resulting from their eventual decommissioning and retirement. Compare the productive output with the total impact of your cloud workloads by reviewing the resources and emissions required per unit of work. Use this data to establish key performance indicators (KPIs), evaluate ways to improve productivity while reducing impact, and estimate the impact of proposed changes over time.

• Establish sustainability goals: For each cloud workload, establish long-term sustainability goals such as reducing the compute and storage resources required per transaction. Model the return on investment of sustainability improvements for existing workloads, and give owners the resources they must invest in sustainability goals. Plan for growth, and architect your workloads so that growth results in reduced impact intensity measured against an appropriate unit, such as per user or per transaction. Goals help you support the wider sustainability goals of your business or organization, identify regressions, and prioritize areas of potential improvement.

• Maximize utilization: Right-size workloads and implement efficient design to verify high utilization and maximize the energy efficiency of the underlying hardware. Two hosts running at 30% utilization are less efficient than one host running at 60% due to baseline power consumption per host. At the same time, reduce or minimize idle resources, processing, and storage to reduce the total energy required to power your workload.

• Anticipate and adopt new, more efficient hardware and software offerings: Support the upstream improvements your partners and suppliers make to help you reduce the impact of your cloud workloads. Continually monitor and evaluate new, more efficient hardware and software offerings. Design for flexibility to permit the rapid adoption of new efficient technologies.

• Use managed services: Sharing services across a broad customer base helps maximize resource utilization, which reduces the amount of infrastructure needed to support cloud workloads. For example, customers can share the impact of common data center components like power and networking by migrating workloads to the AWS Cloud and adopting managed services, such as AWS Fargate for serverless containers, where AWS operates at scale and is responsible for their efficient operation. Use managed services that can help minimize your impact, such as automatically moving infrequently accessed data to cold storage with Amazon S3 Lifecycle configurations or Amazon EC2 Auto Scaling to adjust capacity to meet demand.

• Reduce the downstream impact of your cloud workloads: Reduce the amount of energy or resources required to use your services. Reduce the need for customers to upgrade their devices to use your services. Test using device farms to understand expected impact and test with customers to understand the actual impact from using your services.

Best Practices

• Region selection
• Alignment to demand
• Software and architecture
• Data
• Hardware and services
• Process and culture