Formulation of a scalable and efficient algorithm for replicated databases using causal consistency (Phase 2)

This project addresses the challenges inherent in geo-replication for large-scale data systems, such as those used by major internet services, where the CAP Theorem forces a trade-off between availability and consistency. While distributed replication offers benefits like fault tolerance and low latency, managing consistency remains difficult. The research focuses on leveraging the advantages of causal consistency semantics (causal+), which enables highly available systems while maintaining data consistency, aligning with recent research findings. The methodology begins with a descriptive phase to conceptualize the required theoretical foundations, followed by a deductive phase to determine the properties and design of the algorithm. Subsequently, an evaluation phase will be conducted to verify that the algorithm meets the proposed properties. The ultimate goal is to develop a solution that efficiently manages the necessary metadata to guarantee causal+, potentially requiring a client library to track the causal history of user interactions with the system.<br/><br/><b>Goal</b>: <br/>To formulate a scalable and efficient algorithm for replicated databases using causal consistency semantics, aiming to improve availability without sacrificing data consistency in geo-replicated systems.<br/><br/><b>Research lines</b>: <br/>Artificial intelligence and data mining