NORMA eResearch @NCI Library

Benchmarking Serverless Workloads on Kubernetes

Govind, Hima and González-Vélez, Horacio (2021) Benchmarking Serverless Workloads on Kubernetes. In: 2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid). IEEE, pp. 704-712. ISBN 9781728195865

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As a disruptive paradigm in the cloud landscape, Serverless Computing is attracting attention because of its unique value propositions to reduce operating costs and outsource infrastructure management. Nevertheless, enterprise Functionas-a-Service (FaaS) platforms may pose significant risks such as vendor lock-in, lack of security control due to multi-tenancy, complicated pricing models, and legal and regulatory compliance—particularly in mobile computing scenarios. This work proposes a production-grade fault-tolerant serverless architecture based on a highly-available Kubernetes topology using an open-source framework, deployed on OpenStack instances, and benchmarked with a realistic scaled-down Azure workload traces dataset. By measuring success rate, throughput, latency, and auto scalability, we have managed to assess not only resilience but also sustained performance under a logistic model for three distinct representative workloads. Our test executions show, with 95%–confidence, that between 70 and 90 concurrent users can access the system while experiencing acceptable performance. Beyond the breaking point identified (i.e. 91 transactions per second), the Kubernetes cluster has to be scaled-up or scaled out to meet the QoS and availability requirements.

Item Type: Book Section
Uncontrolled Keywords: Serverless; OpenFaas; High Availability; Workload modelling; Service Level Agreement; SLA; Mobile Computing; Azure; Containers
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Cloud computing
Divisions: School of Computing > Staff Research and Publications
Depositing User: Clara Chan
Date Deposited: 03 Sep 2021 11:04
Last Modified: 03 Sep 2021 11:07

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