AutoGR: Automated Geo-Replication with Fast System Performance and Preserved Application Semantics

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Abstract

Geo-replication is essential for providing low latency response and quality Internet services. However, designing fast and correct geo-replicated services is challenging due to the complex trade-off between performance and consistency semantics in optimizing the expensive cross-site coordination. State-of-the-art solutions rely on programmers to derive sufficient application-specific invariants and code specifications, which is both time-consuming and error-prone. In this paper, we propose an end-to-end geo-replication deployment framework AutoGR (AUTOmated Geo-Replication) to free programmers from such label-intensive tasks. AutoGR enables the geo-replication features for non-distributed, serializable applications in an automated way with optimized performance and correct application semantics. Driven by a novel static analysis tool Rigi, AutoGR can extract invariants for applications by verifying whether their geo-replicated versions obey the serializable semantics of the non-distributed application. Rigi takes application codes as inputs and infers a set of possible side effects and path conditions possibly leading to consistency violations. Rigi employs the Z3 theorem prover to identify pairs of conflicting side effects and feed them to a geo-replication framework for automated across-site deployment. We evaluate AutoGR by transforming four DB-compliant applications that are originally non-replicated to geo-replicated ones across 3 sites. Compared with the state-of-the-art human-intervention-free automated approaches (e.g., for strong consistency), AutoGR reduces up to 61.8% latency and achieves up to 2.12X higher peak throughput; compared with state-of-the-art approaches relying on a manual analysis (e.g., PoR), AutoGR can quickly enable the geo-replication feature with zero human intervention while offering similarly low latency and high peak throughput.