Measuring Streaming System Robustness Using Non-parametric Goodness-of-Fit Tests

被引:0
|
作者
Jamieson, Stuart [1 ]
Forshaw, Matthew [1 ]
机构
[1] Newcastle Univ, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England
来源
COMPUTER PERFORMANCE ENGINEERING, EPEW 2022 | 2023年 / 13659卷
关键词
Streaming system; Robustness; Testing; Non-parametric; DISTRIBUTIONS;
D O I
10.1007/978-3-031-25049-1_1
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Due to unpredictable disturbances in the operating environment, stream processing systems may experience performance degradation and even catastrophic failure. Streaming systems must be robust in the face of such uncertainty in order to be deemed fit for purpose. Measuring and quantifying a system's level of robustness is a non-trivial task. We present, compare and contrast a range of non-parametric goodness-of-fit tests which can act as quantifiers of a system's level of robustness. We show that different tests produce differing relative measures of system robustness, affected by not only the test statistics inherent characteristics, but also by the particular latency percentile under scrutiny.
引用
收藏
页码:3 / 18
页数:16
相关论文
共 50 条
  • [41] A Fourier representation of kernel Stein discrepancy with application to Goodness-of-Fit tests for measures on infinite dimensional Hilbert spaces
    Wynne, George
    Kasprzak, Mikolaj j.
    Duncan, Andrew b.
    BERNOULLI, 2025, 31 (02) : 868 - 893
  • [42] PoweR: A Reproducible Research Tool to Ease Monte Carlo Power Simulation Studies for Goodness-of-fit Tests in R
    de Micheaux, Pierre Lafaye
    Viet Anh Tran
    JOURNAL OF STATISTICAL SOFTWARE, 2016, 69 (03): : 1 - 44
  • [43] Non-parametric tests of productive efficiency with errors-in-variables
    Kuosmanen, Timo
    Post, Thierry
    Scholtes, Stefan
    JOURNAL OF ECONOMETRICS, 2007, 136 (01) : 131 - 162
  • [44] A resampling technique for estimating the power of non-parametric trend tests
    Nordgaard, A
    Grimvall, A
    ENVIRONMETRICS, 2006, 17 (03) : 257 - 267
  • [45] Estimating population genetic parameters and comparing model goodness-of-fit using DNA sequences with error
    Liu, Xiaoming
    Fu, Yun-Xin
    Maxwell, Taylor J.
    Boerwinkle, Eric
    GENOME RESEARCH, 2010, 20 (01) : 101 - 109
  • [46] An assessment of contagion risks in the banking system using non-parametric and Copula approaches
    Toan Luu Duc Huynh
    Nasir, Muhammad Ali
    Sang Phu Nguyen
    Duy Duong
    ECONOMIC ANALYSIS AND POLICY, 2020, 65 : 105 - 116
  • [47] Non-parametric depth-based tests for the multivariate location problem
    Dehghan, Sakineh
    Faridrohani, Mohammad Reza
    AUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, 2021, 63 (02) : 309 - 330
  • [48] A cautionary note on the use of non-parametric tests in the Analysis of Environmental Data
    Modarres, R
    Gastwirth, JL
    Ewens, W
    ENVIRONMETRICS, 2005, 16 (04) : 319 - 326
  • [49] Non-Parametric Clustering Using Deep Neural Networks
    Avgerinos, Christos
    Solachidis, Vassilios
    Vretos, Nicholas
    Daras, Petros
    IEEE ACCESS, 2020, 8 : 153630 - 153640
  • [50] Explaining predictive models using Shapley values and non-parametric vine copulas
    Aas, Kjersti
    Nagler, Thomas
    Jullum, Martin
    Loland, Anders
    DEPENDENCE MODELING, 2021, 9 (01): : 62 - 81