Theodolite: Scalability Benchmarking of Distributed Stream Processing Engines in Microservice Architectures.

Henning, Sören and Hasselbring, Wilhelm (2021) Theodolite: Scalability Benchmarking of Distributed Stream Processing Engines in Microservice Architectures. Big Data Research, 25 (100209). pp. 1-17. DOI 10.1016/j.bdr.2021.100209.

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Abstract

Distributed stream processing engines are designed with a focus on scalability to process big data volumes in a continuous manner. We present the Theodolite method for benchmarking the scalability of distributed stream processing engines. Core of this method is the definition of use cases that microservices implementing stream processing have to fulfill. For each use case, our method identifies relevant workload dimensions that might affect the scalability of a use case. We propose to design one benchmark per use case and relevant workload dimension.

We present a general benchmarking framework, which can be applied to execute the individual benchmarks for a given use case and workload dimension. Our framework executes an implementation of the use case's dataflow architecture for different workloads of the given dimension and various numbers of processing instances. This way, it identifies how resources demand evolves with increasing workloads.

Within the scope of this paper, we present 4 identified use cases, derived from processing Industrial Internet of Things data, and 7 corresponding workload dimensions. We provide implementations of 4 benchmarks with Kafka Streams and Apache Flink as well as an implementation of our benchmarking framework to execute scalability benchmarks in cloud environments. We use both for evaluating the Theodolite method and for benchmarking Kafka Streams' and Flink's scalability for different deployment options.

Document Type: Article
Keywords: Stream processing Microservices Benchmarking Scalability
Research affiliation: Kiel University > Software Engineering
Refereed: Yes
Open Access Journal?: No
Publisher: Elsevier
Related URLs:
Projects: TITAN
Date Deposited: 10 Feb 2021 07:03
Last Modified: 07 Jan 2022 10:05
URI: https://oceanrep.geomar.de/id/eprint/51761

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