Using Analytical Models to Bootstrap Machine Learning Performance Predictors

Abstract

Performance modeling is a crucial technique to enable the vision of elastic computing in cloud environments. Conventional approaches to performance modeling rely on two antithetic methodologies: white box modeling, which exploits knowledge on system's internals and capture its dynamics using analytical approaches, and black box techniques, which infer relations among the input and output variables of a system based on the evidences gathered during an initial training phase. In this paper we investigate a technique, which we name Bootstrapping, which aims at reconciling these two methodologies and at compensating the cons of the one with the pros of the other. We analyze the design space of this gray box modeling technique, and identify a number of algorithmic and parametric trade-offs which we evaluate via two realistic case studies, a Key-Value Store and a Total Order Broadcast service.