Autonomic configuration of HyperDex via analytical modelling

HyperDex is a recent multi-dimensional key-value store that allows efficient search for objects using their secondary attributes. However, the advantage of supporting complex queries comes at the cost of a complex configuration. In this paper we address the problem of automating the configuration of this sort of novel key-value stores. We first show that a misconfiguration may significantly affect the performance of such systems. We then derive a performance model that provides key insights on the behaviour of HyperDex. Based on this model, we derive a technique to automatically and dynamically select the best HyperDex configuration.