Use static stability to prevent bimodal behavior

Designing workloads with static stability is crucial for ensuring consistent performance under both normal and failure scenarios. When workloads exhibit bimodal behavior, it complicates recovery and can lead to increased downtime. By maintaining a single operational mode, you streamline operations and enhance reliability.

Best Practices

Implementing Static Stability in Workloads

• Design your architecture with redundancy and failover mechanisms to ensure seamless operations in case of component failures.
• Use a single normal operational mode for your workload, minimizing complexity and preventing unexpected behavior during failures.
• Regularly test your workload’s resiliency through failure drills and chaos engineering practices to identify potential weak points.
• Monitor and measure system performance proactively to detect anomalies before they lead to failures, ensuring high availability and low MTTR.
• Utilize managed services with built-in redundancy and availability features, such as Amazon RDS or Amazon S3, which can help maintain static stability.

Questions to ask your team

• Have you identified all critical components that could fail and designed redundancy around them?
• Do you use load balancing to distribute traffic among healthy components to prevent overload?
• Have you implemented health checks to automatically detect and replace failing components?
• Does your workload have a defined recovery strategy that is regularly tested?
• Are you monitoring system performance and errors to proactively address potential issues before they affect availability?

Who should be doing this?

Architect

• Design workload architecture that prioritizes reliability and resilience.
• Ensure that the workload operates in a single normal mode.
• Analyze potential failure modes and design recovery strategies accordingly.
• Document the workload’s architecture and operational procedures.
• Collaborate with development teams to implement static stability practices.

DevOps Engineer

• Implement monitoring and alerting systems to detect failures.
• Automate failover and recovery processes to minimize MTTR.
• Test the workload in various scenarios to validate static stability.
• Maintain infrastructure as code to enable consistent deployment practices.
• Work closely with architects to optimize performance and reliability.

Quality Assurance Engineer

• Create test cases that simulate component failures to assess workload reliability.
• Validate that the workload behaves consistently under normal and failure modes.
• Provide feedback on the effectiveness of recovery strategies.
• Coordinate with development teams to ensure testing aligns with architectural goals.

Product Manager

• Define high availability and reliability requirements for the workload.
• Communicate the importance of static stability to stakeholders.
• Prioritize features and improvements that enhance resiliency.
• Oversee the integration of reliability practices into the development lifecycle.

What evidence shows this is happening in your organization?

• Reliability Design Checklist: A comprehensive checklist that guides architects and engineers through the necessary steps to ensure workloads remain statically stable and resilient against component failures.
• Static Stability Policy: A formal policy document that outlines the organization’s commitment to designing workloads with static stability to prevent bimodal behavior, including roles and responsibilities.
• Workload Design Playbook: A playbook that provides strategies and best practices for ensuring workloads are designed for high availability and low MTTR by maintaining static stability.
• Resiliency Strategy Report: An analytical report that evaluates current workloads and proposes enhancements aimed at achieving static stability, including metrics for success.
• Reliability Architecture Diagram: A visual diagram illustrating how workloads should be architected to maintain static stability, highlighting key components and their interactions.

Cloud Services

AWS

• Amazon EC2: Provides resizable compute capacity in the cloud, allowing for easy scaling of instances to maintain stability during component failures.
• Amazon RDS: Managed relational database service that offers high availability configurations to ensure database resiliency and reduced failover times.
• Amazon S3: Object storage service providing durability and reliability for data, ensuring data is always accessible even during component failures.
• AWS Lambda: Serverless compute service that automatically handles scaling and failure recovery, allowing workloads to remain stable under various conditions.
• AWS Elastic Beanstalk: Platform as a service (PaaS) that automatically handles application deployment, scaling, and load balancing, promoting a stable operating environment.

Azure

• Azure Virtual Machines: Offers scalable and flexible computing power, enabling workload adjustments to prevent instability during component failures.
• Azure SQL Database: Managed database service that provides high availability and automated backups, ensuring reliability during failures.
• Azure Blob Storage: Highly durable and available object storage service that protects against data loss, ensuring accessibility during disruptions.

Google Cloud Platform

• Google Compute Engine: Provides scalable and flexible VM instances that can adjust resources dynamically, maintaining reliability under failure conditions.
• Google Cloud SQL: Fully managed relational database service that offers automatic backups and high availability to support resiliency.
• Google Cloud Storage: Reliable and durable object storage service that ensures data is accessible and protected against failures.