An energy utility company operates two primary workloads on Google Cloud: a real-time Smart Grid Telemetry Ingestion microservice and an asynchronous Monthly Customer Billing Report Generator. Failure of real-time telemetry ingestion immediately threatens power grid stability, whereas delayed billing reports can be re-processed asynchronously within a 24-hour window without business loss. Which strategy correctly aligns technical operational metrics with business impact for these workloads?
- Define a stringent availability SLO (such as 99.99%) evaluated over short windows with burn-rate alerts for grid telemetry, and a lower SLO (such as 99.0%) based on job completion freshness over a monthly window for billing reports.Cevap
- BEnforce a uniform 99.99% availability SLO across both workloads because they run on the same shared Google Kubernetes Engine cluster.
- CDesignate the 99.99% availability target itself as the SLI for real-time telemetry and configure static alerts whenever error rates briefly spike.
- DEstablish a 99.999% SLA to end-user customers for billing report generation to ensure high customer satisfaction before defining internal SLOs and SLIs.
Cevap
Define a stringent availability SLO (such as 99.99%) evaluated over short windows with burn-rate alerts for grid telemetry, and a lower SLO (such as 99.0%) based on job completion freshness over a monthly window for billing reports.
The correct strategy differentiates the Service Level Objectives based on business impact: high-frequency real-time grid telemetry requires tight availability goals and rapid error-budget alert monitoring, while batch report generation only requires periodic freshness targets within a 24-hour window.
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Aligning SLAs, SLOs, and SLIs with Business Impact