Understanding Cloud Workloads Performance in a Production like Environment

TL;DR

This paper classifies cloud workloads based on resource impact and studies how load, hyper-threading, and resource limits affect performance, providing insights to optimize cloud system utilization.

Contribution

It introduces a workload taxonomy and presents three key studies on performance factors affecting cloud workloads in production-like environments.

Findings

Load level significantly affects tail latency.

Hyper-threading impacts workload performance.

Resource limiting can improve system utilization.

Abstract

Understanding inter-VM interference is of paramount importance to provide a sound knowledge and understand where performance degradation comes from in the current public cloud. With this aim, this paper devises a workload taxonomy that classifies applications according to how the major system resources affect their performance (e.g., tail latency) as a function of the level of load (e.g., QPS). After that, we present three main studies addressing three major concerns to improve the cloud performance: impact of the level of load on performance, impact of hyper-threading on performance, and impact of limiting the major system resources (e.g., last level cache) on performance. In all these studies we identified important findings that we hope help cloud providers improve their system utilization.