Author
Baransel, Berrak Alara, Peker, Alper, Balkıs, Hilmi Ömer, Arı, İsmail
Publication Date
2021
Publication Place
-
Springer
Subject
Cloud, Container, Django, Docker, Jmeter, Kubernetes, Load testing
Type
Document
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
978-303071710-0
Record ID
a0f8391b-63e4-4c05-85cf-ab1f6abf966c
Library Location
Computer Science
Date
2021
Notes
Ozyegin University ; Saha Information Technologies
Sample Text
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.
DOI
10.1007/978-3-030-71711-7_24
Cilt
1382
