Volume 11, Number 1

Using Semi-supervised Classifier to Forecast Extreme CPU Utilization

  Authors

Nitin Khosla¹ and Dharmendra Sharma², ¹ICTCAPM, Australia and ²University of Canberra, Australia

  Abstract

A semi-supervised classifier is used in this paper is to investigate a model for forecasting unpredictable load on the it systems and to predict extreme CPU utilization in a complex enterprise environment with large number of applications running concurrently. This proposed model forecasts the likelihood of a scenario where extreme load of web traffic impacts the it systems and this model predicts the CPU utilization under extreme stress conditions. The enterprise it environment consists of a large number of applications running in a real time system. Load features are extracted while analysing an envelope of the patterns of work-load traffic which are hidden in the transactional data of these applications. This method simulates and generates synthetic workload demand patterns, run use-case high priority scenarios in a test environment and use our model to predict the excessive CPU utilization under peak load conditions for validation. Expectation maximization classifier with forced-learning, attempts to extract and analyse the parameters that can maximize the chances of the model after subsiding the unknown labels. As a result of this model, likelihood of an excessive cpu utilization can be predicted in short duration as compared to few days in a complex enterprise environment. Workload demand prediction and profiling has enormous potential in optimizing usages of it resources with minimal risk.

  Keywords

Semi-Supervised Learning, Performance Engineering, Load And Stress Testing, Machine Learning.