Ramya R Moorthy – Workload Modelling Essentials for Performance Engineers
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Everything you need to KNOW to run realistic Performance Tests
This unique course aims to provide technical insights on Workload Modelling concepts & techniques for carrying out realistic performance tests for web applications (& web services). As Workload Modelling is the most important activity in the Performance test life cycle, which helps to improve the accuracy of the performance tests by simulating the actual end user access patterns, this unique course covers the essentials for Performance Testers / Engineers on the workload modelling techniques.
The course teaches the need for workload model & how performance test results can go wrong, if wrong workload is used to carry-out performance tests. It covers the workload modeling techniques to be considered for applications already running in production & for newly developed applications.
The course covers detailed insights on production traffic analysis using Google Analytics & DeepLogAnalyzer tools & discusses techniques to create qualitatively & quantitatively valid workload using production user loads for conducting performance tests. The course includes various case studies to understand the key metrics to be derived from peak hour traffic analysis, to identify the key use cases for performance testing, to validate the workload model using Little’s law, etc. It provides case study reference excel sheets on how to validate the load tests conducted on various tools like JMeter / Load Runner using Little’s law.
The course also introduces various statistical distributions & details Poisson distribution analysis which forms the basis for advanced workload modelling analysis.
This course forms the basic pre-requisite for taking advanced courses as having a accurate workload model is important step in performance modelling.
Your Instructor
Ramya R Moorthy brings over 15+ years of industry wide experience in Performance Engineering & Testing space. She has extensive experience doing consulting for Performance Testing & Engineering engagements for several clients including Honeywell, Shell, ING, Logitech, Comcast, PGE, MetLife, JPMC, KPMG, KeyBank, etc across business domains solving technical problems for assuring their system for its performance, scalability, availability & capacity. She has led several senior leadership roles in her experience working for product & service based companies. She has great passion for learning & experimentation. She provides technical consulting services in NFR testing space. Her key area of interest includes application capacity sizing, performance modeling & predictive performance analytics. She is also a certified ethical hacker. She has great zeal towards teaching & mentoring the young professionals to build elite professionals for future.
She is a conference speaker & well known writer. She has published several papers, authored many articles & e-books in several journals & LinkedIn. Some of the recent conferences where she has won accolades for her papers include QAI-STC , CMG India, NFTCON India & CMG US on topics related to capacity planning, statistical modeling, use of machine learning techniques for anomaly detection, performance forecasting, etc. She is a best paper award winner in CMG India 2016 whitepaper contest & Mullen award holder (rewarded for technical excellence and an engaging presentation style) for her paper presented at CMG US 2017 on Anomaly detection using machine learning/statistical techniques.
She is a computer science engineering (BE) graduate with her Masters in Software Systems (MS) from BITS PILANI University, India. She currently serves in CMG India Board of Directors.
For any course related technical queries, you can reach out to her : [email protected]
Course Co-Author
Ruslan Desyatnikov founder and CEO of QA Mentor brings over 20 years of Quality Assurance, Quality Control, Process Improvement, Software Testing and Performance Engineering experience helping many Fortune 500 companies including HSBC, Citi, Morgan Stanley and others . Ruslan graduated from Baruch College – City University of New York with a BBA in Computer Information Systems, and holds MBA in Technology Management from Phoenix University of Arizona. He holds a number of Quality Assurance/Testing and Project Management certifications and is an active board member of multiple QA Organizations in the United States and Europe. Ruslan’s articles are published in many popular magazines such as CIOReview, Outsourcing Gazette, Tea-Time with Testers, Stickyminds, LinkedIn, Outsource Gazette and multiple QA blogs.
Ruslan was among 3 finalists of Champion of The Year Award by The European Software Testing Awards in 2015, award given to the individual who has championed the cause of software testing above all others. Ruslan also was among finalists for Test Manager of The Year award by The European Software Testing Awards in 2016. In 2016, Ruslan also received an award “50 Most Creative CEOs to Watch” by InsightSuccess magazine.
Course Curriculum
Workload Modelling Essentials for Performance Engineers
Section 1 : Introduction to Workload Modelling
Lesson #1 : Refreshing Performance Testing Basics (8:12)
Lesson #2 : What are Non-Functional Requirements (6:17)
Lesson #3 : Performance testing in DevOps (3:25)
Lesson #4 : What is a Workload Model ? (10:07)
Lesson #5 : Introduction to Workload Modelling (5:41)
Lesson #6 : A quick look at Performance Testing Metrics & its Relationships (12:51)
Lesson #7 : Simple Workload model – Quick Examples (17:36)
Lesson #8 : Workload Modelling Versus Performance Modelling (2:10)
Section 2 – Workload Modelling Strategies for New Applications
Lesson #1 : Workload Modelling Strategies Overview (9:32)
Lesson #2 : Use Case Prioritization analysis (3:59)
Lesson #3 : Using Customer Behavior Modelling Graph (6:04)
Lesson #4 : Using User Community Modelling Language (5:15)
Lesson #5 : Workload Model of a Call center application (5:13)
Section 3 – Workload Modelling Strategies for existing Applications in Production
Lesson #1 : Workload Modelling Strategies Overview (10:34)
Lesson #2 : A closer look at Peak hour traffic analysis (7:15)
Lesson #3 : Google Analytics Log Analysis – Case Study Discussion (20:40)
Lesson #4 : Web Log Analysis – Case Study Discussion (44:07)
Lesson #5 : Workload Modelling for Web services (5:36)
Section 4 – Workload Modelling Case studies
Lesson #1 : Workload Modelling Case study 1 (13:04)
Lesson #2 : Workload Modelling Case study 2 (6:30)
Lesson #3 : Workload Modelling Case study 3 (4:10)
Lesson #4 : Workload Modelling Case study 4 (12:35)
Lesson #5 : Workload Modelling Case study 5 (7:27)
Lesson #6 : Workload Modelling Case study 6 (9:25)
Lesson #7 : Workload Modelling Case study 7 (3:05)
Lesson #8 : Workload Modelling Case study 8 (5:46)
Lesson #9 : Creating JMeter Test Plan using Workload Model (5:25)
Lesson #10 : Key mistakes in workload modelling (9:42)
Section 5 – Introduction to Statistical Distributions (Road Ahead)
Lesson #1 : Introduction to Statistical Distributions (8:33)
Lesson #2 : Statistical Terminologies (8:00)
Lesson #3 : Poisson Distribution Analysis Example (11:17)
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