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In-memory (transactional) data stores are recognized as a first-class datamanagement technology for cloud platforms, thanks to their ability to match theelasticity requirements imposed by the pay-as-you-go cost model. On the otherhand, defining the well-suited amount of cache servers to be deployed, and thedegree of in-memory replication of slices of data, in order to optimizereliability/availability and performance tradeoffs, is far from being a trivialtask. Yet, it is an essential aspect of the provisioning process of cloudplatforms, given that it has an impact on how well cloud resources are actuallyexploited. To cope with the issue of determining optimized configurations ofcloud in-memory data stores, in this article we present a flexible simulationframework offering skeleton simulation models that can be easily specialized inorder to capture the dynamics of diverse data grid systems, such as thoserelated to the specific protocol used to provide data consistency and/ortransactional guarantees. Besides its flexibility, another peculiar aspect ofthe framework lies in that it integrates simulation and machine-learning(black-box) techniques, the latter being essentially used to capture thedynamics of the data-exchange layer (e.g. the message passing layer) across thecache servers. This is a relevant aspect when considering that the actualdata-transport/networking infrastructure on top of which the data grid isdeployed might be unknown, hence being not feasible to be modeled via white-box(namely purely simulative) approaches. We also provide an extended experimentalstudy aimed at validating instances of simulation models supported by ourframework against execution dynamics of real data grid systems deployed on topof either private or public cloud infrastructures.

A Flexible Framework for Accurate Simulation of Cloud In-Memory Data Stores