Optimizing hyperspace hashing via analytical modelling and adaptation

Abstract

Hyperspace hashing is a recent multi-dimensional indexing technique for distributed key-value stores that aims at supporting efficient queries using multiple objects' 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 innovative distributed indexing mechanism. We first show that a misconfiguration may significantly affect the performance of the system. We then derive a performance model that provides key insights on the behaviour of hyperspace hashing. Based on this model, we derive a technique to automatically and dynamically select the best configuration.