Chasing the Optimum in Replicated In-Memory Transactional Platforms via Protocol Adaptation

Replication plays an essential role for in-memory distributed transactional platforms, given that it represents the primary means to ensure data durability. Unfortunately, no single replication technique can ensure optimal performance across a wide range of workloads and system configurations. This paper tackles this problem by presenting MORPHR, a framework that allows to automatically adapt the replication protocol of in-memory transactional platforms according to the current operational conditions. MORPHR presents two key innovative aspects. On one hand, it allows to plug in, in a modular fashion, specialized algorithms to regulate the switching between arbitrary replication protocols. On the other hand, MORPHR relies on state of the art machine learning techniques to autonomously determine the best replication in face of varying workloads. We integrated MORPHR in an open-source in-memory NoSQL data grid, and evaluated it by means of an extensive experimental study. The results highlight that MORPHR is accurate in identifying the best replication strategy in presence of complex realistic workloads, and does so with minimal overhead.