Experimental Evaluation of the Cloud-Native Application Design

Brunner, Sandro; Blöchlinger, Martin; Toffetti, Giovanni; Spillner, Josef; Bohnert, Thomas Michael

doi:10.1109/ucc.2015.87

Cited by 16 publications

(8 citation statements)

References 4 publications

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“…While several scalability evaluations describe scalability as functions of resources, many of them do not conduct isolated experiments for different load intensities (Brunner et al 2015;Al-Said Ahmad and Andras 2019;Karakaya et al 2017;Nasiri et al 2019;Karimov et al 2018). Instead, they continuously increase the load on a system and measure when SLOs cannot be fulfilled anymore.…”

Section: Scalability Measurement Methodsmentioning

confidence: 99%

“…In our previous work, we highlighted limitations of this method for the case of stream processing (Henning and Hasselbring 2021a). Sometimes also the provisioned resources are auto-scaled in the background (Brunner et al 2015;Al-Said Ahmad and Andras 2019), which serves a different purpose than our scalability benchmarking method. Different methods for scalability benchmarking of database systems in the cloud are discussed by Kuhlenkamp et al (2014), which, however, do not include running isolated experiments for different load resource combinations.…”

Section: Scalability Measurement Methodsmentioning

confidence: 99%

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A configurable method for benchmarking scalability of cloud-native applications

Henning

Hasselbring

2022

Empir Software Eng

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Cloud-native applications constitute a recent trend for designing large-scale software systems. However, even though several cloud-native tools and patterns have emerged to support scalability, there is no commonly accepted method to empirically benchmark their scalability. In this study, we present a benchmarking method, allowing researchers and practitioners to conduct empirical scalability evaluations of cloud-native applications, frameworks, and deployment options. Our benchmarking method consists of scalability metrics, measurement methods, and an architecture for a scalability benchmarking tool, particularly suited for cloud-native applications. Following fundamental scalability definitions and established benchmarking best practices, we propose to quantify scalability by performing isolated experiments for different load and resource combinations, which asses whether specified service level objectives (SLOs) are achieved. To balance usability and reproducibility, our benchmarking method provides configuration options, controlling the trade-off between overall execution time and statistical grounding. We perform an extensive experimental evaluation of our method’s configuration options for the special case of event-driven microservices. For this purpose, we use benchmark implementations of the two stream processing frameworks Kafka Streams and Flink and run our experiments in two public clouds and one private cloud. We find that, independent of the cloud platform, it only takes a few repetitions (≤ 5) and short execution times (≤ 5 minutes) to assess whether SLOs are achieved. Combined with our findings from evaluating different search strategies, we conclude that our method allows to benchmark scalability in reasonable time.

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Section: Scalability Measurement Methodsmentioning

confidence: 99%

Section: Scalability Measurement Methodsmentioning

confidence: 99%

A configurable method for benchmarking scalability of cloud-native applications

Henning

Hasselbring

2022

Empir Software Eng

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“…The diversity of involved components calls for several different monitoring approaches, yet, a holistic view of the entire application is needed for the efficient maintenance of CNAs. Brunner et al [147] indicate that CNAs are designed with inherent support for self-management, scalability, and resilience across clustered units of application logic. The authors acknowledge that while Cloud-native systematic design is promising, no dominant application development model exists for CNAs.…”

Section: Cloud Native Application Monitoring and Observabilitymentioning

confidence: 99%

A Reference Architecture for Observability and Compliance of Cloud Native Applications

Pourmajidi¹,

Zhang²,

Steinbacher³

et al. 2023

Preprint

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The evolution of Cloud computing led to a novel breed of applications known as Cloud-Native Applications (CNAs). However, observing and monitoring these applications can be challenging, especially if a CNA is bound by compliance requirements.To address this challenge, we explore the characteristics of CNAs and how they affect CNAs' observability and compliance. We then construct a reference architecture for observability and compliance pipelines for CNAs. Furthermore, we sketch instances of this reference architecture for single-and multi-cloud deployments.The proposed architecture embeds observability and compliance into the CNA architecture and adopts a "battery-included" mindset. This architecture can be applied to small and large CNA deployments in regulated and non-regulated industries. It allows Cloud practitioners to focus on what is critical, namely building their products, without being burdened by observability and compliance requirements. This work may also interest academics as it provides a building block for generic CNA architectures.

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“…However, cloud-native applications [8] are characterized by a quite more complex and often distributed architecture. Differently from traditional monolithic applications, cloud-native ones are often composed of a number of different services or microservices [21] that may be deployed on the same or different machines.…”

Section: Introductionmentioning

confidence: 99%

Fault Tolerance in Real-Time Cloud Computing

Abeni

Andreoli

Gustafsson

et al. 2023

2023 IEEE 26th International Symposium on Real-Time Distributed Computing (ISORC)

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This paper presents the Fault-Tolerant Real-Time Cloud (FTRTC) project that aims to design cloud computing infrastructures capable of hosting highly reliable and real-time applications. These applications are characterized by strict timing and reliability constraints, as well as critical failure scenarios. For instance, such requirements are commonly found in the context of Industry 4.0. We present a formalization of the problem of designing real-time cloud applications supporting an adjustable level of fault tolerance throughout their distributed execution in a cloud infrastructure. The contributions presented in this paper indicate important research directions when building cloud infrastructures able to supporting ultra-reliable real-time applications.

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Experimental Evaluation of the Cloud-Native Application Design

Cited by 16 publications

References 4 publications

A configurable method for benchmarking scalability of cloud-native applications

A configurable method for benchmarking scalability of cloud-native applications

A Reference Architecture for Observability and Compliance of Cloud Native Applications

Fault Tolerance in Real-Time Cloud Computing

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