Self-Tuning Transactional Data Grids: The Cloud-TM Approach

In this paper we focus on the problem of self-tuning distributed transactional cloud data stores by presenting an overview of the autonomic mechanisms integrated in the Cloud-TM platform, a transactional cloud data store developed in the context of a recent European project. Cloud-TM takes a holistic approach to self-tuning and elastic scaling, treating them as strongly intertwined problems with the ultimate goals of i) achieving optimal efficiency at any scale of the platform, and ii) minimizing resource consumption in presence of varying workloads. From a methodological perspective, this is achieved by relying on the innovative idea of exploiting the diversity of different modelling approaches, including analytical models, machine-learning and simulations. By employing these modelling techniques in synergy, the Cloud-TM platform can dynamically optimize the underlying distributed data store over a number of dimensions, including its scale, the strategy it adopts to distribute and replicate data among the platforms´ nodes, as well as its replication protocol.