Performance Modelling of Partially Replicated In-Memory Transactional Stores

This paper presents PROMPT, a PeRfOrmance Model for Partially replicated in-memory Transactional cloud stores. PROMPT combines white box Analytical Modelling and Machine Learning techniques, with the goal of achieving the best of the two methodologies: low training times, high extrapolation power, and portability across heterogeneous cloud infrastructures. We validate PROMPT via an extensive experimental study based on a popular open-source transactional in-memory data store (Red Hat´s Infinispan), industry-standard benchmarks, and deployments on both public and private cloud infrastructures.