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Thursday, March 24, 2022

Our poster presentation "SPEC Research — Introducing the #PredictiveAnalytics Working Group" is scheduled at #ICPE2022 #ICPEconf Poster & Demo (Monday - April 11, 2022, 5:15pm)


    https://icpe2022.spec.org/program_files/schedule/

Wednesday, March 16, 2022

I am happy to co-author 2 papers for #ICPE2022 #ICPEconf

Online conference program  https://icpe2022.spec.org/program_files/schedule/  scheduled our following  presentations:

Poster & Demo (Monday - April 11, 2022, 5:15pm )

André Bauer, Mark Leznik, Md Shahriar Iqbal, Daniel Seybold, Igor Trubin, Benjamin Erb, Jörg Domaschka and Pooyan Jamshidi. SPEC Research — Introducing the Predictive Data Analytics Working Group

Data Challenge (Tuesday - April 12,, 4:15pm - 4:55pm)

Md Shahriar Iqbal, Mark Leznik, Igor Trubin, Arne Lochner, Pooyan Jamshidi and André Bauer. Change Point Detection for MongoDB Time Series Performance Regression



Monday, February 28, 2022

"Change Point Detection (#ChangeDetection) for MongoDB Time Series Performance Regression" paper for ACM/SPEC ICPE 2022 Data Challenge Track

The ACM/SPEC ICPE 2022 - Data Challenge Track Committee has decided to ACCEPT our article:

TITLE: Change Point Detection for MongoDB Time Series Performance Regression
AUTHORS: Md Shahriar Iqbal, Mark Leznik, Igor Trubin, Arne Lochner, Pooyan Jamshidi and André Bauer



ABSTRACT
Commits to the MongoDB software repository trigger a collection
of automatically run tests. Here, the identification of commits 
responsible for performance regressions is paramount. Previously, the
process relied on manual inspection of time series graphs to identify
signi￿cant changes, later replaced with a threshold-based detection
system. However, neither system was sufficient for finding changes
in performance in a timely manner. This work describes our recent
implementation of a change point detection system built upon the
Perfomalist approach in combination with XGBoost algorithm. The
algorithm produces a list of change points representing significant
changes from a given history of performance results. We are able
to automatically detect change points and achieve an 83% accuracy,
all while reducing the human effort in the process.

More Perfomalist's  approach details can be found in this blog post:

Wednesday, February 9, 2022

My Cloud Optimization team at #CapitalOne bank won the CMG.org #Innovation Award (#CMGNews)

  https://www.cmg.org/2022/02/capital-one-announced-as-winner-of-the-impact-innovation-award/




Thursday, February 3, 2022

My publications in RG got 5000+ reads

https://www.researchgate.net/profile/Igor-Trubin 



Friday, January 21, 2022

Panel Discussion: Roadmap for Cultivating Performance-Aware Software Engineers

 

"#CloudServers Rightsizing with #Seasonality Adjustments" - my presentation at CMG IMPACT conference (#CMGnews)


Feb 4, 2022 12:15 Virtual at https://cmgimpact.com/sessions-schedule/

Thursday, January 6, 2022

"Performance Anomaly and Change Point Detection for Large-Scale System Management" - my paper published at Springer

 


Intelligent Sustainable Systems pp 403-407Cite as

Performance Anomaly and Change Point Detection for Large-Scale System Management

Conference paper
  • 1Downloads
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 334)

Abstract

The presentation starts with the short overview of the classical statistical process control (SPC)-based anomaly detection techniques and tools including Multivariate Adaptive Statistical Filtering (MASF); Statistical Exception and Trend Detection System (SETDS), Exception Value (EV) meta-metric-based change point detection; control charts; business driven massive prediction and methods of using them to manage large-scale systems such as on-prem servers fleet or massive clouds. Then, the presentation is focused on modern techniques of anomaly and normality detection, such as deep learning and entropy-based anomalous pattern detections.

Keywords

Anomaly detection Change point detection Business driven forecast Control chart Deep Learning Entropy analysis 

References

  1. 1.
    Trubin, I.: Exception based modeling and forecasting. In: Proceedings of Computer Measurement Group (2008)Google Scholar
  2. 2.
    Jeffrey Buzen, F., Annie Shum, S.: MASF—multivariate adaptive statistical filtering. In: Proceedings of Computer Measurement Group (1995)Google Scholar
  3. 3.
    Trubin, I.: Review of IT control chart. CIS J. 4(11), 2079–8407 (2013)Google Scholar
  4. 4.
    Perfomalist Homepage, http://www.perfomalist.com. Last accessed on 10 June 2021
  5. 5.
    Trubin, I., et al.: Systems and methods for modeling computer resource metrics. US Patent 10,437,697 (2016)Google Scholar
  6. 6.
    Trubin, I.: Capturing workload pathology by statistical exception detection. In: Proceedings of Computer Measurement Group (2005)Google Scholar
  7. 7.
    Loboz, C.: Quantifying imbalance in computer systems. In: Proceedings of Computer Measurement Group (2011)Google Scholar