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PAC 2019 virtual Hemalatha Murugesan

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Predictive Performance Modeling using Machine Learning (ML) Algorithms

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PAC 2019 virtual Hemalatha Murugesan

  1. 1. Predictive Performance Modeling Using Machine Learning (ML) Algorithms By Hemalatha Murugesan & Madhu Tanikella
  2. 2. The Magical Stone
  3. 3. Predictive Modelling Need • Insights into IT systems behavior (non-functional) ahead • Better predictability of ➢ System responsiveness ➢ Infrastructure utilizations • Ability to plan for hardware in advance for Mergers & Acquisitions / Country Rollouts / New Features etc. • Minimize surprises w.r.t. performance & scalability
  4. 4. Predictive Performance Modeling 4 What? Predict Quality of Service (QoS) of IT systems (response time, throughput, server utilization) for varying conditions such as increase in # of users / transaction load / hardware configurations - by making use of available data points from Production / QA and forecast How it helps? Data-driven decision making for additional hardware procurement for future workloads Effective hardware utilization Provides leads for quicker tuning / optimization of specific layers of Application Landscape Reduces cost & time to carry out multiple trial-error performance runs KEY CONSIDERATIONS  Not a replacement for performance benchmarking / Application tuning – complements them  More the data - more accurate the predictions  Analytical models are cost effective – can be implemented in multiple ways Thumb Rules Linear Projections Analytical Models Simulation Techniques Real Environment Accuracy Cost Performance Prediction Techniques QNM ML Empirical Formula
  5. 5. Key Components ofMachine Learning for Performance Modeling 5 Trained Data Set Test Data Set ML-based Performance models Prediction results
  6. 6. Predictive Performance Modeling 6
  7. 7. Thank You!

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