USING SEMI-SUPERVISED CLASSIFIER TO FORECAST EXTREME CPU UTILIZATION
Nitin Khosla1 and Dharmendra Sharma2
1Assistant Director- Performance Engineering, ICTCAPM, Dept. of Home Affairs, Canberra, Australia
2Professor – Computer Science, University 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.
Original Source Link :
Artificial Intelligence & Applications - Research 2017-2018 