A FRAMEWORK FOR QUANTIFYING AND OPTIMIZING ERROR BUDGETS IN MULTI-REGION CLOUD DEPLOYMENTS

Abstract

Site reliability engineering uses error budgets—the permissible amount of unreliability implied by a service-level objective (SLO)—to balance feature velocity against reliability. In multi-region cloud deployments, however, error budgeting becomes substantially more complex: regional failures, correlated outages, cross-region dependencies, and routing decisions all shape the effective reliability delivered to users, yet most practice still treats the error budget as a single global number. This study proposes a framework for quantifying and optimizing error budgets in multi-region deployments that models per-region reliability, inter-region correlation, and traffic routing to compute an accurate composite budget and to allocate it optimally across regions. The framework decomposes the global budget into regional sub-budgets, accounts for correlated failure through a dependency factor, and formulates budget allocation as a constrained optimization minimising expected budget burn subject to cost. It was evaluated using a simulation of a three-region active-active service under diverse failure and traffic scenarios across 1,200 runs. Results show that naïve global error budgeting overestimated available reliability headroom by up to 38% when correlated regional failures were present, leading to premature budget exhaustion. The proposed correlation-aware framework reduced SLO-breach surprise events by 82% and, through optimized allocation, achieved the target availability at 19% lower redundancy cost than uniform allocation. The findings demonstrate that treating the error budget as region-aware and correlation-aware materially improves both reliability predictability and cost efficiency. The study contributes a reproducible quantitative framework and allocation method for reliability engineering in multi-region cloud systems.