Beauty and the beast: A case study on performance prototyping of data-intensive containerized cloud applications

TL;DR

This paper presents a case study on performance prototyping for data-intensive containerized cloud applications, focusing on creating reference use cases, generating reliable workloads, and analyzing performance variability in Kubernetes environments.

Contribution

It introduces a reference use case for data-intensive cloud applications, utilizes ProtoCom for workload generation, and evaluates performance variability and scalability in a Kubernetes setting.

Findings

ProtoCom shows high variability in cloud environments.

Performance correlates with node occupancy.

Scalability analysis under autoscaling policy.

Abstract

Data-intensive container-based cloud applications have become popular with the increased use cases in the Internet of Things domain. Challenges arise when engineering such applications to meet quality requirements, both classical ones like performance and emerging ones like elasticity and resilience. There is a lack of reference use cases, applications, and experiences when prototyping such applications that could benefit the research community. Moreover, it is hard to generate realistic and reliable workloads that exercise the resources according to a specification. Hence, designing reference applications that would exhibit similar performance behavior in such environments is hard. In this paper, we present a work in progress towards a reference use case and application for data-intensive containerized cloud applications having an industrial motivation. Moreover, to generate reliable CPU workloads we make use of ProtoCom, a well-known library for the generation of resource demands, and report the performance under various quality requirements in a Kubernetes cluster of moderate size. Finally, we present the scalability of the current solution assuming a particular autoscaling policy. Results of the calibration show high variability of the ProtoCom library when executed in a cloud environment. We observe a moderate association between the occupancy of node and the relative variability of execution time.