A multi‐faceted analysis of the performance variability of virtual machines

Abstract

AbstractCloud computing and virtualization solutions allow one to rent the virtual machines (VMs) needed to run applications on a pay‐per‐use basis, but rented VMs do not offer any guarantee on their performance. Cloud platforms are known to be affected by performance variability, but a better understanding is still required. This article moves in that direction and presents an in‐depth, multi‐faceted study on the performance variability of VMs. Unlike previous studies, our assessment covers a wide range of factors: 16 VM types from 4 well‐known cloud providers, 10 benchmarks, and 28 different metrics. We present four new contributions. First, we introduce a new benchmark suite (VMBS) that let researchers and practitioners systematically collect a diverse set of performance data. Second, we present a new indicator, called VI, that allows for measuring variability in the performance of VMs. Third, we illustrate an analysis of the collected data across four different dimensions: resources, isolation, time, and cost. Fourth, we present multiple predictive models based on machine learning (ML) that aim to forecast future performance and detect time patterns. Our experiments provide important insights on the resource variability of VMs, highlighting differences and similarities between various cloud providers. To the best of our knowledge, this is the widest analysis ever conducted on the topic.