Computing at the Edge: Redefining database elasticity for distributed cloud architectures

This article introduces a novel framework for database elasticity in distributed cloud architectures, addressing the fundamental limitations of traditional elasticity models when deployed across heterogeneous edge environments. While conventional approaches assume uniform computing capabilities and reliable network connectivity, modern distributed systems face unique challenges, including intermittent connectivity, variable computing power, and data gravity constraints that significantly impact workload mobility. The article presents a comprehensive edge elasticity model that reconceptualizes database scaling for these complex environments, providing both theoretical foundations and practical implementation strategies. The proposed EdgeDB architecture implements adaptive resource allocation mechanisms, connectivity-aware consistency models, and predictive scaling algorithms specifically designed for distributed deployments. The article demonstrates robust performance despite infrastructure variability, offering significant advantages over traditional elasticity models as computing environments become increasingly distributed. This article extends distributed systems theory by challenging binary CAP theorem constraints and establishing a mathematical framework for multi-objective optimization in environments where constraints vary both spatially and temporally, providing database architects with concrete strategies for designing systems that can effectively operate across the entire computing spectrum from cloud to edge.