Exploiting Causality to Engineer Elastic Distributed Software

Abstract

This goal of this paper is to anchor elasticity in terms of causality in distributed applications. Assuming a large-scale distributed application architected as a set of interacting components, we motivate the need for (1) analyzing causality between interactions, and (2) estimating casual probability that an increase in the frequency of interaction i1 can increase the frequency of interaction i2 caused by i1. We present algorithms to estimate causality and causal probability by combining well known sampling, program analysis, path profiling and dynamic slicing algorithms. We apply our algorithms for causal probability to three large-scale distributed applications, to evaluate (a) their effectiveness in the timely provisioning and de-provisioning of compute resources and (b) whether causality and causal probability present a fundamental and widely-applicable way of engineering auto-elasticity.