Model building for dynamic multi-tenant provider environments

Abstract

Increasingly, storage vendors are finding it difficult to leverage existing white-box and black-box modeling techniques to build robust system models that can predict system behavior in the emerging dynamic and multi-tenant data centers. White-box models are becoming brittle because the model builders are not able to keep up with the innovations in the storage system stack, and black-box models are becoming brittle because it is increasingly difficult to a priori train the model for the dynamic and multi-tenant data center environment. Thus, there is a need for innovation in system model building area. In this paper we present a machine learning based blackbox modeling algorithm called M-LISP that can predict system behavior in untrained region for these emerging multitenant and dynamic data center environments. We have implemented and analyzed M-LISP in real environments and the initial results look very promising. We also provide a survey of some common machine learning algorithms and how they fare with respect to satisfying the modeling needs of the new data center environments.