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/

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

"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

https://icpe2022.spec.org/tracks-and-submissions/data-challenge-track/

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:

CPD - Change Point Detection (#ChangeDetection) is implemented in the free web tool Perfomalist

The result of initial usage of Perfomalist CPD API against MongoDB data is published HERE:

Perfomalist #ChangeDetection API was used against #MongoDB #perfomanceTesting dataset

 

 

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/

My publications in RG got 5000+ reads

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

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/

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

 

Intelligent Sustainable Systems pp 403-407| Cite as

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

• Authors
• Authors and affiliations

• Igor Trubin

1. 1.

Conference paper

First Online: 17 December 2021

• 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 

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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
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   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