SRE without fools and with examples on Azure.

Introduction

The main problem I see with the acronym SRE is that many people do not understand what problems these practices solve and what they were created for. To understand this, you need to dive into the concept of Site Reliability Engineering and understand how and why it arose.

The origins and purpose of SRE

SRE, or Site Reliability Engineering, was developed by Google in the early 2000s. The main goal of this approach was to solve problems related to the reliability and stability of complex distributed systems. In traditional IT infrastructures, there is often a conflict between developers, who want to quickly make changes and release new features, and operations teams, who are responsible for the stability and reliability of the system.

SRE solves this problem by applying engineering principles and practices to tasks typically handled by system administrators. This includes automation of routine tasks, real-time system monitoring, and incident management. The main goal of SRE is to provide a balance between high development speed and system stability, which is especially important in the conditions of constantly growing demands for digital services.

What SRE is not

SRE (Site Reliability Engineering) is not just an operational role or a replacement for DevOps. This is not a solution to all IT infrastructure problems and not only for large companies. SRE encompasses more than technical aspects, including organizational and cultural change. In Azure, SRE is not limited to monitoring with Azure Monitor, CI/CD with Azure DevOps, or incident management with Azure Service Health, but includes comprehensive engineering approaches to improve system reliability and performance.

One of the key concepts in SRE is managing reliability through Service Level Agreements (SLAs) and Service Level Objectives (SLOs). Understanding these concepts is critical to the successful application of SRE practices. SRE is an approach that helps ensure the reliable and stable operation of software by combining the principles of development and operational work, with an emphasis on automation and incident management.

SLA and SLO: What are they?

• SLA (Service Level Agreement) is a formal agreement between a service provider and a customer that defines the expected level of service. The SLA includes metrics against which performance is measured and penalties for not meeting those metrics. For example, SLA might guarantee that a website will be available at least 99.9% of the time per month.
• SLO (Service Level Objectives) are internal goals that are set to achieve the service level defined by the SLA. SLO are more specific and measurable goals that teams use to track and improve system performance. For example, if the SLA requires 99.9% availability, the SLO might set a goal of 99.95% availability to provide some margin.
• SLI (Service Level Indicator) is an indicator used to measure specific aspects of service quality and performance, such as response time, availability or error rate. This is one of the key components in managing the reliability and efficiency of IT systems. SLI (Service Level Indicator) is a key term that helps measure the quality of service by providing specific metrics to evaluate its performance. For example, SLI may include service response time or percentage of successful requests.

Why 100% is not correct

In the real world, achieving 100% availability or performance is virtually impossible and economically impractical. Aiming for 100% leads to excessive infrastructure and support costs that are rarely justified from a business perspective. Instead, SRE sets realistic SLO that ensure a high level of reliability while taking into account acceptable risks and costs.

Expectation

Properly set SLO help manage expectations both within the team and with customers. They help define what is considered an acceptable level of service and help teams focus on the most critical aspects of reliability and performance. This helps prevent unnecessary efforts to achieve unattainable goals and focus on the real needs of users and business.

 Examples of using SLO and SLA in Azure

Azure Service Level Agreements (SLA)

Azure provides various SLA, for example, guaranteeing the availability of virtual machines up to 99.9% and databases up to 99.99% of the time.

Azure Monitor

Azure Monitor allows you to configure and track SLO, such as monitoring response times and application error rates.

Azure Application Insights

The Azure Monitor part is for monitoring web applications. Helps track metrics such as response time and percentage of successful requests.

Azure Service Health

A tool for receiving notifications about the status of Azure services, including scheduled work and incidents, helping to manage user expectations.

Azure Ping Test

A tool for checking the availability and latency of network connections, helping to improve performance and meet SLO and SLA.

It is important to understand that in any system, regardless of its stability, errors and failures will inevitably occur.

Error Budget

What is Error Budget?

Error Budget is the acceptable amount of errors or downtime that can be accepted during a certain period without violating SLO (Service Level Objectives).

Example

For an SLO of 99.9% availability, the Error Budget is 0.1% (that is, accordingly, 43.2 minutes of downtime per month). If the application leads to incidents and causes 30 minutes of downtime, you have 13.2 minutes of Error Budget left, allowing you to continue implementing new features. When running out of Error Budget, focus on improving reliability.

Why is an Error Budget needed?

1. Balance between innovation and stability : Allows teams to find the optimal balance between releasing new features and maintaining system stability.
2. Transparency and Measurability : Provides a measurable way to evaluate system performance.
3. Team Motivation : Motivates development and admin teams to work together to maintain a balance between quality and speed of development. If you notice that your bug budget is running low, it means you know what to focus on.

Using Error Budget in Azure

1. Defining the SLO and Error Budget : Set the SLO, for example, 99.9% availability, and define the Error Budget as 0.1%.
2. Monitoring with Azure Monitor : Monitor performance and availability metrics, configure dashboards and alerts.
3. Diagnostics with Azure Application Insights : Analyze errors and their causes.
4. Incident management with Azure Service Health : Receive notifications about the status of services and quickly respond to incidents.
5. Corrective actions : When the Error Budget threshold is reached, suspend the deployment of new features to the industrial environment and focus on improving reliability.

Conclusion

Error Budget helps find a balance between innovation and stability. Using Azure Monitor, Azure Application Insights, and Azure Service Health, you can effectively manage and monitor your Error Budget, ensuring reliable application performance. It is not necessary to spend the entire Error Budget, but if it is not used, it may indicate excessive conservatism in implementing new changes and innovations.

In the field of IT operations management, it is important to separate routine work that does not bring long-term value from tasks that contribute to the development and improvement of the system.

Toil

What is Toil?

Toil is routine, repetitive operational work that does not add long-term value and can be automated.

Why do you need to reduce Toil?

1. Increased efficiency : Reducing Toil allows teams to focus on important tasks.
2. Improving team satisfaction : Less routine, more motivation.
3. Increasing system reliability : Automation reduces the likelihood of errors.

Using Azure tools to reduce Toil

1. Azure Automation
2. Azure DevOps
3. Azure Logic Apps
4. Azure Functions
5. Azure Monitor

Example

To automate virtual machine updates, use Azure Automation to create a Runbook that checks, installs updates, and reboots the OS, reducing manual work.

Toil are repetitive, manual, and non-creative tasks that do not add long-term value and can be automated. This does not apply to innovative or strategic tasks that require a creative approach and intellectual engagement.

Conclusion

Toil is a routine job that can be automated. Azure tools such as Azure Automation, Azure DevOps, Azure Logic Apps, Azure Functions, and Azure Monitor help reduce Toil, freeing up time for more important tasks.

In IT operations management, it is important to distinguish between individual Toil and organizational Toil . Individual Toil refers to routine tasks performed by a specific worker, distracting him from more important and creative tasks. Organizational Toil covers common routine processes in an organization that reduce the overall effectiveness of a team or department. Reducing both individual and organizational Toil allows you to increase the productivity and efficiency of the entire organization.

What to do with all this?

Monitoring (Monitoring) and observability (Observability)

What is monitoring and observation?

• Monitoring : The process of collecting, analyzing, and displaying data about system health and performance.
• Observability : The ability of a system to provide detailed information about its internal workings through metrics, logs, and traces to facilitate diagnosis and troubleshooting.

Why is monitoring and observation needed?

1. Increased reliability : Enable timely detection and elimination of problems.
2. Performance improvement : Help optimize system performance.
3. Accelerate diagnostics : Provide access to detailed information for rapid resolution of incidents.

Using Azure tools for monitoring and observability

1. Azure Monitor
2. Azure Application Insights
3. Azure Log Analytics
4. Azure Service Health
5. Azure Network Watcher

Conclusion

Monitoring and observability are key elements in maintaining high reliability and performance of systems. Azure tools such as Azure Monitor, Azure Application Insights, Azure Log Analytics, Azure Service Health, and Azure Network Watcher help provide effective monitoring that allows you to quickly identify and remediate issues.

RED signals and MTTR

For effective system monitoring and management, it is important to pay attention to RED signals (Rates, Errors, Duration), which provide basic insights into system operation. For example, increasing the level of errors (Errors) in the API may signal problems with new functions. This helps to respond quickly to problems.

Reduction of MTTR (Mean Time to Recovery) is an important KPI that allows you to evaluate the effectiveness of recovery processes and improve them. For example, automating recovery processes reduces incident resolution time, which can reduce MTTR by 30%.

In the field of IT operations, the terms Monitoring , APM , and Telemetry are often used , but they have different meanings and purposes. Monitoring is the process of monitoring systems and applications in real time to detect problems and failures. APM (application performance management) focuses on measuring and improving application performance. Telemetry is the collection, transmission and analysis of system performance data for deeper insights. Understanding the difference between these concepts helps you manage your IT infrastructure more effectively.

Observability is a key aspect of effective IT systems management. Observability allows you to understand the internal state of the system using external signals such as metrics, logs, and traces. This helps to quickly diagnose and solve problems. It is important to avoid an excess of alerts that can overwhelm the team. Instead, it is better to have a single but critical alert that really indicates a serious problem. This approach increases the efficiency of response and reduces the risk of missing important incidents.

Fragile and Antifragile

What are Fragile and Antifragile?

• Fragile : Systems that break easily under pressure or change. They cannot withstand stress and become less effective or fail completely.
• Antifragile : Systems that improve under stress, change, or instability. They adapt and become more resilient.

Why Antifragile systems are needed?

1. Resistance to failures : Antifragile systems withstand stress and even improve, which increases overall reliability.
2. Flexibility and adaptability : Such systems are better able to cope with changes and unforeseen situations.
3. Long-term performance : Antifragile systems become more productive and reliable over time

There are several key metrics that help evaluate a system's performance and its ability to recover from incidents:

• MTTD (Mean Time to Detect) — the average time to detect a problem. This metric shows how quickly a team can identify a problem or incident.
• MTTR (Mean Time to Recovery) — the average time for recovery. This metric measures how long it takes for a system to recover from an incident.
• MTRS (Mean Time to Restore Service) — average service restoration time. Shows how long it takes for the service to return to normal operation after a failure.
• SLO (Service Level Objective) — target service level. These are specific metrics that define goals for service quality, such as response time or availability.
• RPO (Recovery Point Objective) — data recovery point. Defines the maximum acceptable data loss in the event of an incident, indicating how often data should be backed up.

Conclusion Fragile and Antifragile

Antifragile systems are the key to increasing stability and reliability. Azure tools like Auto-Scaling, Traffic Manager, Site Recovery, Chaos Studio, and Backup help you build systems that not only withstand stress and change, but also get better over time.

Conclusion

In conclusion, it is worth noting the importance of collaboration and raising the level of all participants in the process. This includes implementing standards for designing systems that are ready for SRE processes from the outset. This approach ensures efficient operation and reduces the number of problems that may arise in the future.

Finally, the impact of artificial intelligence (AI) on SRE is worth mentioning. In new times, new challenges arise for SRE in AI-based services, which we will consider in the following articles. AI opens up new opportunities, but also requires adaptation of existing approaches to ensure the reliability and stability of systems.

Conclusion

In conclusion, it is worth noting the importance of collaboration and raising the level of all participants in the process. This includes the implementation of standards for system development that are ready for SRE processes from the very beginning. Such an approach ensures efficient operation and reduces the number of problems that may arise in the future.

Finally, it is worth mentioning the impact of artificial intelligence (AI) on SRE. In new times, new challenges arise for SRE in AI-based services, which we will consider in subsequent articles. AI opens up new opportunities but also requires the adaptation of existing approaches to ensure the reliability and stability of systems.