Key influencing factors of the Kubernetes auto-scaler for computing-intensive microservice-native cloud-based applications. (February 2020)

• Record Type: Journal Article
• Authors: Taherizadeh, Salman; Grobelnik, Marko
• Is Part Of: Advances in engineering software. Volume 140(2020)
• Publication Date: 2020-02
• Subjects: Auto-scaling -- Key factors -- Microservices -- Kubernetes -- Computing-intensive services -- Cloud-based applications; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2019.102734
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: Proposing a set of new influencing factors to be considered in the dynamic management of scalable resources provided by container orchestration platforms. Defining an extend of the Kubernetes auto-scaler for computing-intensive microservice-native cloud-based applications. Performance evaluation conducted under different predictable and unpredictable workload scenarios. Abstract: Nowadays, different types of computing-intensive services such as mechanical, aerospace, civil and environmental applications are often deployed on the cloud since it offers a convenient on-demand model for renting resources and easy-to-use elastic infrastructures. Moreover, the modern software engineering disciplines exploit orchestration tools such as Kubernetes to run cloud applications based on a set of microservices packaged in containers. On the one hand, in order to ensure the users' experience, it is necessary to allocate enough number of container instances before the workload intensity surges at runtime. On the other hand, renting expensive cloud-based resources can be unaffordable over a long period of time. Therefore, the choice of a reactive auto-scaling method may significantly affect both response time and resource utilisation. This paper presents a set of key factors which should be considered in the development of auto-scaling methods. Through a set of experiments, a discussion follows to help shed light on how such factors influence the performance of auto-scaling methods under different workload conditions such as on-and-off, predictable and unpredictable bursting workload patterns. Due to suitable results, the proposed set of key factors are exploited in the PrEstoCloud software system for microservice-native cloud-based computationally-intensive applications.