Evaluating the Effectiveness of Information Extraction in Real-World Storage Management

As storage deployments within enterprises continue to grow, there is an increasing need to simplify and automate. Existing tools for automation rely on extracting information in the form of device models and workload patterns from raw performance data collected from devices. This paper evaluates the effectiveness of applying such information extraction techniques on realworld data collected over a period of months from the data centers of two commercial enterprises. Real-world monitor data has several challenges that typically do not exist in controlled lab environments. Our analysis for creating models is using popular algorithms such as M5, CART, ARIMA and Fast Fourier Transform (FFT). The relative error rate in predicting device response time from real-world data is 40

• Type: Conference
• Year: 2008
• Where: MASCOTS
• Authors: Aameek Singh, Sandeep Uttamchandani, Yin Wang
• Keywords: Controlled Lab Environment, MASCOTS 2008, Modeling And Simulation, Relative Error, Relative Error Rate