Rely on the data plane and not the control plane during recovery

Architecting workloads for high availability and reducing mean time to recovery (MTTR) involves designing them to be resilient against component failures. Utilizing the data plane rather than the control plane when responding to failures ensures operational continuity and minimizes reliance on resource administration during critical periods.

Best Practices

Utilize Data Plane Operations for Resiliency

• Design systems to prioritize data plane actions during recovery scenarios. This ensures that your workload can quickly recover from failures without relying heavily on control plane operations.
• Implement automated failover mechanisms within the data plane to minimize recovery time and prevent manual intervention. This could involve health checks and traffic rerouting to standby instances.
• Leverage managed services that abstract the control plane complexity, allowing for streamlined recovery processes that focus on data plane interactions.
• Minimize the dependency on control plane API calls during incidents, as these can be slow or unresponsive. Instead, ensure that your architecture can tolerate transient failures and maintain service availability.
• Conduct regular chaos engineering exercises to test how your system responds during failures, fine-tuning your data plane operations to ensure robust resiliency.
• Ensure comprehensive monitoring and alerting for the data plane to quickly identify and respond to failures, allowing for immediate remediation without needing to engage the control plane.

Questions to ask your team

• How does your workload leverage data plane operations during failure recovery?
• What mechanisms are in place to ensure that data plane traffic continues uninterrupted during component failures?
• Can you describe an instance where you relied on the data plane for recovery? What were the outcomes?
• How do you minimize the use of control plane operations during degradation events?
• What monitoring tools do you use to detect when a data plane operation needs to be triggered?
• How have you tested the resiliency of your workload against component failures?

Who should be doing this?

Architect

• Design the workload with high availability in mind.
• Ensure resilience through the use of data planes for recovery processes.
• Analyze and evaluate the recovery strategy to minimize the use of control plane operations during outages.
• Document architectures with a focus on data plane capabilities for operational resilience.

DevOps Engineer

• Implement the recovery mechanisms ensuring they prioritize data plane interactions.
• Monitor the workload and make adjustments based on performance and failure incidents.
• Automate recovery processes utilizing data plane features effectively.
• Conduct testing of recovery scenarios to validate the effectiveness of the implemented strategies.

Site Reliability Engineer (SRE)

• Establish metrics for monitoring reliability and MTTR.
• Develop incident response plans that leverage the data plane.
• Conduct post-incident reviews focusing on improvements in data plane-driven recovery.
• Collaborate with architects to refine resiliency strategies based on operational data.

Product Owner

• Define reliability requirements based on user needs and service level objectives (SLOs).
• Prioritize features and improvements that enhance workload resiliency.
• Work with the development team to ensure alignment on recovery strategies focusing on data planes.

What evidence shows this is happening in your organization?

• Disaster Recovery Playbook: A comprehensive playbook detailing the steps to recover workloads using data plane operations. This document outlines roles, responsibilities, and specific data plane commands and procedures necessary for quick recovery during component failures.
• Resiliency Strategy Template: A template to help teams define their approach to workload design focused on resiliency. It includes considerations for leveraging data plane operations during failures, ensuring that the control plane is minimally involved in recovery processes.
• Reliability Checklists: Checklists providing guidance on best practices to follow while configuring workloads. It emphasizes reliance on data planes for recovery and includes verification steps to ensure that the architecture aligns with the reliability objectives.
• Incident Response Dashboard: A live dashboard that visualizes the health of production workloads and incidents. The dashboard focuses on monitoring data plane metrics that indicate traffic health and recovery operations, enabling swift decisions during outages.
• Recovery Simulation Model: A model used to simulate various failure scenarios within the workload architecture. It includes exercises that validate the effectiveness of relying on data planes for recovery, demonstrating expected MTTR improvements.

Cloud Services

AWS

• Amazon EC2 Auto Scaling: Automatically adjusts the number of EC2 instances in response to demand, ensuring that your application can withstand failures.
• Amazon Route 53: Provides DNS failover capabilities, allowing traffic to be routed away from unhealthy endpoints swiftly.
• AWS Lambda: Enables running code in response to events from other AWS services, allowing for rapid recovery and handling of failures without relying heavily on control plane operations.
• Amazon S3: Offers high durability and availability for storing data, allowing for reliable retrieval of data during recovery scenarios.

Azure

• Azure Virtual Machine Scale Sets: Allows you to manage and scale a group of load-balanced VMs automatically, ensuring availability during component failures.
• Azure Traffic Manager: Distributes network traffic to the best performing or most available endpoints, handling failover for application availability.
• Azure Functions: Provides serverless compute capabilities that run code in response to events with minimal reliance on control plane during recovery.
• Azure Blob Storage: Ensures durability and availability for large amounts of unstructured data, supporting disaster recovery efforts.

Google Cloud Platform

• Google Kubernetes Engine (GKE): Automatically manages the deployment and scaling of containerized applications, handling failures efficiently.
• Google Cloud Load Balancing: Distributes traffic across instances, helping to ensure high availability and resiliency during failures.
• Cloud Functions: Allows you to run event-driven serverless functions, reducing the reliance on control plane actions during incident response.
• Google Cloud Storage: Provides highly durable storage for objects, facilitating data availability in recovery scenarios.