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Kubernetes distributions for the edge: serverless performance evaluation

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Abstract

Serverless computing, especially when deployed at the edge of the network, is seen as an enabling technology for the future development of more complex Internet of Things systems. However, special care must be taken when deploying new edge infrastructures for serverless workloads in terms of resource usage and network connectivity. Inefficient utilization of the available computing resources might easily cancel out the benefits acquired by moving the equipment closer to the edge, namely the reduced communication latency. Containers, together with the Kubernetes container orchestrator, are used by many serverless platforms today. We evaluate the performance of three different Kubernetes distributions—full-fledged Kubernetes, K3s, and MicroK8s when deployed in a resource constrained environment at the edge. We use the OpenFaaS serverless platform and employ 14 different benchmarks divided into three separate categories to evaluate various aspects of the execution performance of the distributions. Four different test types are performed focusing on cold start latency, serial execution performance, parallel execution using a single replica, and parallel execution utilizing different autoscaling strategies. Our results show that the edge-oriented K3s and MicroK8s distributions offer better performance in the majority of the tests, while a full-fledged deployment exhibits noticeable advantages for sustained loads such as parallel function invocation using a single replica.

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Notes

  1. The extreme outliers for the K3s platforms (larger than 17s) have been cut off to aid the visibility of the figure. However, they have been taken into account in all other analyses and calculations.

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Acknowledgements

The work presented in this paper has received funding from the Faculty of Computer Science and Engineering under the “SCAP” project.

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Correspondence to Vojdan Kjorveziroski.

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Kjorveziroski, V., Filiposka, S. Kubernetes distributions for the edge: serverless performance evaluation. J Supercomput 78, 13728–13755 (2022). https://doi.org/10.1007/s11227-022-04430-6

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