Discovering dynamic dependencies in enterprise environments for problem determination

被引:0
|
作者
Gupta, M [1 ]
Neogi, A
Agarwal, MK
Kar, G
机构
[1] IBM India Res Lab, New Delhi, India
[2] IBM Watson Res Ctr, New York, NY USA
来源
关键词
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
In order to reduce mean time to recovery (MTTR) in heterogeneous enterprise environments it should be possible to easily and quickly determine the root cause of a problem detected at a higher level, e.g. through response time violation of a transaction category, and resolve it. Many problem determination applications use a component dependency graph to pinpoint the root cause. However, such graphs are often manually constructed. This paper introduces a simple nonintrusive technique based on mining of existing runtime monitored data, to construct a dynamic dependency graph between the components of an enterprise environment. The graph is traversed to identify nodes that are the cause of response time related problems.
引用
收藏
页码:221 / 233
页数:13
相关论文
共 50 条
  • [31] A Statistical Perspective on Discovering Functional Dependencies in Noisy Data
    Zhang, Yunjia
    Guo, Zhihan
    Rekatsinas, Theodoros
    [J]. SIGMOD'20: PROCEEDINGS OF THE 2020 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, 2020, : 861 - 876
  • [32] Discovering context-aware conditional functional dependencies
    Du, Yuefeng
    Shen, Derong
    Nie, Tiezheng
    Kou, Yue
    Yu, Ge
    [J]. FRONTIERS OF COMPUTER SCIENCE, 2017, 11 (04) : 688 - 701
  • [33] Discovering Low-Dimensional Descriptions of Multineuronal Dependencies
    Mitskopoulos, Lazaros
    Onken, Arno
    [J]. ENTROPY, 2023, 25 (07)
  • [34] Discovering Code Dependencies by Harnessing Developer's Activity
    Konopka, Martin
    Navrat, Pavol
    Bielikova, Maria
    [J]. 2015 IEEE/ACM 37TH IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, VOL 2, 2015, : 801 - 802
  • [35] Discovering and validating cancer genetic dependencies: approaches and pitfalls
    Ann Lin
    Jason M. Sheltzer
    [J]. Nature Reviews Genetics, 2020, 21 : 671 - 682
  • [36] A discrete probabilistic memory model for discovering dependencies in time
    Hochreiter, S
    Mozer, MC
    [J]. ARTIFICIAL NEURAL NETWORKS-ICANN 2001, PROCEEDINGS, 2001, 2130 : 661 - 668
  • [37] Ekklesia as enterprise: Discovering the Church at work
    Pryfogle, Daniel
    [J]. REVIEW & EXPOSITOR, 2018, 115 (03) : 372 - 377
  • [38] seq2graph: Discovering Dynamic Non-linear Dependencies from Multivariate Time Series
    Dang, Xuan-Hong
    Shah, Syed Yousaf
    Zerfos, Petros
    [J]. 2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2019, : 1774 - 1783
  • [39] Enterprise continuous integration using binary dependencies
    Roberts, M
    [J]. EXTREME PROGRAMMING AND AGILE PROCESSES IN SOFTWARE ENGINEERING, PROCEEDINGS, 2004, 3092 : 194 - 201
  • [40] Inference Rules of Semantic Dependencies in the Enterprise Modelling
    Gustas, Remigijus
    [J]. NEW TRENDS IN SOFTWARE METHODOLOGIES, TOOLS AND TECHNIQUES, 2005, 129 : 235 - 251