Scalable and Efficient Algorithm for Replicated Databases Using Causal Consistency

This project addresses the challenges inherent in geo-replication for large internet services, where the demand for low latency and high availability conflicts with the limitations imposed by the CAP Theorem. While models like causal consistency (Causal+) offer a more relaxed and advantageous semantic, their implementation demands significant metadata management and a complex client library to track the full causal history. The primary goal is to design an algorithm that is both scalable and efficient for database replication under causal consistency. The methodology involves a descriptive phase to establish the state of the art, followed by a deductive phase to define the algorithm's properties and design. Finally, an experimental phase will validate that the proposed properties are met, culminating in the publication of the results in a JCR scientific journal.<br/><br/><b>Goal</b>: <br/>To formulate a scalable and efficient algorithm for replicated databases using causal consistency, aiming to improve data availability and consistency in geo-replicated systems.<br/><br/><b>Research lines</b>: <br/>Geographic information systems