Abstract
Providing end-users with high quality e-commerce, online communication, education services requires careful performance monitoring, tuning and prediction under heavy traffic loads. To address this issue, we propose and evaluate a novel methodology using Docker containers for load testing. Our experience over several benchmarks, local machines vs. Cloud, and web servers suggest that load testing as a service requires a multi-dimensional optimization over slave counts, network latencies, bandwidth, and traffic patterns and there are opportunities for learning these parameters that can later be modelled into a smart load testing algorithm, with machine learning at the driver seat. Beyond the ease and speed of deployment, containers and cloud also provide a low cost alternative to load testing; we completed our cloud experiments by spending only $10. The only disadvantage of public clouds can be their centralized nature and distance to real customer bases.
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Acknowledgements
We would like to thank Suayip Ozmen, Erdi Olmezogullari, Selcuk Sozuer and Zeynep Ozdemir Guler from Saha Information Technologies and Doga Yilmaz from Ozyegin University for their valuable technical comments and support.
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Baransel, B.A., Peker, A., Balkis, H.O., Ari, I. (2021). Towards Low Cost and Smart Load Testing as a Service Using Containers. In: Yildirim Yayilgan, S., Bajwa, I.S., Sanfilippo, F. (eds) Intelligent Technologies and Applications. INTAP 2020. Communications in Computer and Information Science, vol 1382. Springer, Cham. https://doi.org/10.1007/978-3-030-71711-7_24
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