In practice, this is accomplished by monitoring the amount of operational work being done by SREs, and redirecting excess operational work to the product development teams: reassigning bugs and tickets to development managers, [re]integrating developers into on-call pager rotations, and so on. The redirection ends when the operational load drops back to 50% or lower.

The hero jack-of-all-trades on-call engineer does work, but the practiced on-call engineer armed with a playbook works much better. While no playbook, no matter how comprehensive it may be, is a substitute for smart engineers able to think on the fly, clear and thorough troubleshooting steps and tips are valuable when responding to a high-stakes or time-sensitive page.

The business or the product must establish the system’s availability target. Once that target is established, the error budget is one minus the availability target. A service that’s 99.99% available is 0.01% unavailable. That permitted 0.01% unavailability is the service’s error budget. We can spend the budget on anything we want, as long as we don’t overspend it.

Monitoring is one of the primary means by which service owners keep track of a system’s health and availability. As such, monitoring strategy should be constructed thoughtfully.

Reliability is a function of mean time to failure (MTTF) and mean time to repair (MTTR) [Sch15]. The most relevant metric in evaluating the effectiveness of emergency response is how quickly the response team can bring the system back to health—that is, the MTTR.

In general, for any software service or system, 100% is not the right reliability target because no user can tell the difference between a system being 100% available and 99.999% available.

When they are focused on operations work, on average, SREs should receive a maximum of two events per 8–12-hour on-call shift. This target volume gives the on-call engineer enough time to handle the event accurately and quickly, clean up and restore normal service, and then conduct a postmortem. If more than two events occur regularly per on-call shift, problems can’t be investigated thoroughly and engineers are sufficiently overwhelmed to prevent them from learning from these events.

In general, an SRE team is responsible for the availability, latency, performance, efficiency, change management, monitoring, emergency response, and capacity planning of their service(s).

Therefore, Google places a 50% cap on the aggregate "ops" work for all SREs—tickets, on-call, manual tasks, etc. This cap ensures that the SRE team has enough time in their schedule to make the service stable and operable.

By design, it is crucial that SRE teams are focused on engineering. Without constant engineering, operations load increases and teams will need more people just to keep pace with the workload.

What exactly is Site Reliability Engineering, as it has come to be defined at Google? My explanation is simple: SRE is what happens when you ask a software engineer to design an operations team.

Google has chosen to run our systems with a different approach: our Site Reliability Engineering teams focus on hiring software engineers to run our products and to create systems to accomplish the work that would otherwise be performed, often manually, by sysadmins.