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ASE 2021
Sun 14 - Sat 20 November 2021 Australia
Thu 18 Nov 2021 09:00 - 09:20 at Kangaroo - Development Chair(s): James C. Davis

To effectively utilize cloud computing, cloud practice and research require accurate knowledge of the performance of cloud applications. However, due to the random performance fluctuations, obtaining accurate performance results in the cloud is extremely difficult. To handle this random fluctuation, prior research on cloud performance testing relied on a non-parametric statistic tool called bootstrapping to design their stop criteria. However, in this paper, we show that the basic bootstrapping employed by prior work overlooks the internal dependency within cloud performance test data, which leads to inaccurate performance results.

We then present Metior, a novel automated cloud performance testing methodology, which is designed based on statistical tools of block bootstrapping, law of large numbers, and autocorrelation. These statistical tools allow Metior to properly consider the internal dependency within cloud performance test data. They also provide better coverage of cloud performance fluctuation and reduce the testing cost. Experimental evaluation on two public clouds showed that 98% of Metior’s tests could provide performance results with less than 3% error. Metior also significantly outperformed existing cloud performance testing methodologies in terms of accuracy and cost – with up to 14% increase in the accurate test count and up to 3.1 times reduction in testing cost.

Thu 18 Nov

Displayed time zone: Hobart change

09:00 - 10:00
DevelopmentIndustry Showcase / Research Papers / NIER track at Kangaroo
Chair(s): James C. Davis Purdue University, USA
Performance Testing for Cloud Computing with Dependent Data Bootstrapping
Research Papers
Sen He The University of Texas at San Antonio, Tianyi Liu The University of Texas at San Antonio, Palden Lama The University of Texas at San Antonio, Jaewoo Lee University of Georgia, In Kee Kim University of Georgia, Wei Wang University of Texas at San Antonio, USA
Privacy as first-class requirements in software development: A socio-technical approach
NIER track
Itsik Benbenisty University of Haifa, Irit Hadar University of Haifa, Gil Luria University of Haifa, Paola Spoletini Kennesaw State University
Towards a Serverless Java Runtime
Industry Showcase
Yifei Zhang Alibaba Group, Tianxiao Gu Alibaba Group, Xiaolin Zheng Alibaba Group, Lei Yu Alibaba Group, Wei Kuai Alibaba Group, Sanhong Li Alibaba Inc.