In this course designed by performance and capacity expert Dr. Igor Trubin, learners will participate in lectures and hands-on activities to be familiar with:
- Machine learning based Anomaly Detection technique
- Classical (SPC) and MASF (For system performance data) Control Chartirting
- Where is the Control Chart Used?
- What are the types of Control Charts?
- Reading, building, and interpreting Control Charts
- Typical cases of real world issues captured by anomaly detection system (VMs, Mainframes, Middleware, E2E response and more)
- How to build free AWS cloud server with R and build there control charts
- Performance anomaly (Perfomaly) detection system R implementation example (SEDS-lite - open source based tool)
- Performance Anomaly Detection - concept
- Detecting Performance Anomalies ("Perfomalies") by Control Charts
- Detecting Performalies by IT-Charts
- Setting up Free AWS Public Cloud EC2 Server with R-Studio to Develop and Run Simple Performaly Detection Programs
- Practice 1: Examples and Exercises (using Spreadsheet)
- Practice 2: Examples and Exercises (using R on AWS cloud)
- Final Consideration - Build your Own Performaly Detection System!
The follow-up course, Intermediate Performance Anomaly Detection, will include:
- Detecting Novelties in performance data by using Exception Value (EV) approach (“knee” detection like)
- Detecting Normality in the performance workload data by neural nets and deep learning using R or Python packages
- Detecting Anomalous short living objects in cloud by using entropy calculation using R (AWS ASG and EC2 count perfomalies)
- ... and more..
I started my career in 1979 as an IBM/370 system engineer. In 1986 I got my PhD. in Robotics at St. Petersburg Technical University (Russia) and then worked as a professor teaching there CAD/CAM, Robotics and Computer Science for about 12 years. I published 30 papers and made several presentations for international conferences related to the Robotics, Artificial Intelligent and Computer fields. In 1999 I moved to the US and worked at Capital One bank in Richmond as a Capacity Planner. My first CMG paper was written and presented in 2001. The next one, "Global and Application Level Exception Detection System Based on MASF Technique," won a Best Paper award at CMG 2002 and was presented again at UKCMG 2003 in Oxford, England. My CMG 2004 paper about applying MASF technique to mainframe performance data was republished in the IBM z/Series Expo. I also presented my papers in Central Europe CMG conference and in numerous US regional meetings. I continue to enhance my exception detection methodologies. After working more than 2 years as the Capacity Management team lead for IBM, I had worked for SunTrust Bank for 3 years and then got back to IBM holding for 2+ years Sr. IT Architect position. Currently I work for Capital One bank as IT Manager for IT Capacity Management group. In 2015 I have been elected to the CMG (http://www.cmg.org) board of directors.
StartWhere Control Charts are Used in IT
PreviewWhy the Control Chart is Used for Capacity Management
StartWhat is a Control Chart
StartMASF, SPC Control and Histogram Charts - How They Work
StartDifferent Types of Control Charts to Show Different Perfomalies
StartWhy is it so Powerful?
StartReading Control Charts: Memory and CPU Metrics
StartMainframe Performance Metrics
StartOther Metrics and Cases (VMs, I/Os, Response time)
StartQuiz for Section 2