A Client-Centric Consistency Model for Distributed Data Stores using Colored Petri Nets

The aim of increased reliability and performance in system architectures has resulted in the development of data replication systems. However, preserving consistency during concurrent requests across various replicated data servers presents a significant challenge, especially when write operations are involved. This issue highlights the necessity of establishing a precise balance between system performance and consistency, particularly in the context of large-scale distributed databases. Large-scale distributed databases must navigate a trade-off between performance and consistency. Client-centric (CC) Consistency addresses this problem by guaranteeing consistency for single-client access to distributed data stores (DDS). CC consistency encompasses four distinct types of consistency assurances: 1) monotonic read, 2) monotonic write, 3) read your writes, and 4) write follow reads. In this paper, we present a formal model encapsulating CC consistency and its associated consistency models within distributed systems. Our model uses high-level ML functions to represent CC consistency and its models. Integrity is verified CPN tools, ensuring consistency. We demonstrate its effectiveness in discerning supported CC consistencies with relevant inputs. This formal approach aids understanding and assists in optimizing distributed system performance while maintaining consistency.