Using Semi-supervised Classifier to Forecast Extreme CPU Utilization
| Autor: | Nitin Khosla, Dharmendra Sharma |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Demand patterns 05 social sciences CPU time Workload 010501 environmental sciences computer.software_genre 01 natural sciences Web traffic 0502 economics and business Expectation–maximization algorithm Central processing unit Data mining 050207 economics Transaction data computer Real-time operating system 0105 earth and related environmental sciences |
| Zdroj: | International Journal of Artificial Intelligence & Applications. 11:45-52 |
| ISSN: | 0976-2191 |
| DOI: | 10.5121/ijaia.2020.11104 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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