Performance of Data Mining, Media, and Financial Applications under Private Cloud Conditions
| Autor: | Carlos A. F. Maron, Adriano Vogel, Anderson M. Maliszewski, Luiz Gustavo Fernandes, Dalvan Griebler, Claudio Schepke |
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| Rok vydání: | 2018 |
| Předmět: |
Finance
020203 distributed computing Computer science business.industry 020206 networking & telecommunications Cloud computing 02 engineering and technology Virtualization computer.software_genre Resource (project management) Software deployment Kernel (statistics) Container (abstract data type) 0202 electrical engineering electronic engineering information engineering Data mining business computer |
| Zdroj: | ISCC |
| DOI: | 10.1109/iscc.2018.8538759 |
| Popis: | This paper contributes to a performance analysis of real-world workloads under private cloud conditions. We selected six benchmarks from PARSEC related to three mainstream application domains (financial, data mining, and media processing). Our goal was to evaluate these application domains in different cloud instances and deployment environments, concerning container or kernel-based instances and using dedicated or shared machine resources. Experiments have shown that performance varies according to the application characteristics, virtualization technology, and cloud environment. Results highlighted that financial, data mining, and media processing applications running in the LXC instances tend to outperform KVM when there is a dedicated machine resource environment. However, when two instances are sharing the same machine resources, these applications tend to achieve better performance in the KVM instances. Finally, financial applications achieved better performance in the cloud than media and data mining. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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