With high performance becoming a mandate, its impact & need for sophisticated performance management is realized by every e-business. Though Application Performance Management (APM) tools has brought down the performance problem diagnosis time to a great extend, these tools don't actually help in detecting the anomalies in the production environment (online or offline mode) and make forecasts on the server performance metrics for capacity sizing. Hence, robust performance anomaly detection and forecasting solution is in demand to detect anomalies in production environment and to provide forecasts on server resource demand to support in server sizing. This paper deals with the implementation of Performance Anomaly detection and Forecasting Model for an online retailer business application using statistical modeling & machine learning techniques that has yielded multi-fold benefit to the business
Presenter bio: I carry about 14+ years of industry wide experience in Performance Testing & Performance Engineering. Am a computer science engineer with Masters in Software Systems (MS) from BITS PILANI, India. I am the Co-Founder & CTO of a US startup, QAEliteSouls LLC (http://qaelitesouls.com) and the founder of a Indian startup, EliteSouls Consulting Services LLP (http://elitesouls.in). I am the CMG India Director for the year 2017.
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