出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：The aim of semi-supervised learning approach in this paper is to improve the supervised
classifiers to investigate a model for forecasting unpredictable load on system and to predict
CPU utilization in a big enterprise applications environment. This model forecasts the
likelihood of a burst in web traffic to the IT systems and predicts the CPU utilization under
stress conditions. The enterprise IT infrastructure consists of many enterprise applications
running in a real time system. Load features are extracted while analyzing the patterns of
work- load demand which are hidden in the transactional data of applications. This approach
generates synthetic workload patterns, execute use-case scenarios in the test environment and
use our model to predict the excessive utilization of the CPU behavior under peak load and
stress conditions for the validation purpose. Expectation Maximization method with colearning,
attempts to extract and analyze the parameters that maximize the likelihood of the
model after subsiding the unknown labels. As a result of this approach, likelihood of excessive
CPU utilization can be predicted in few hours as compared to few days. Workload profiling and
prediction has enormous potential to optimize the usages of IT resources with low risk.
