Forecast Extreme CPU Usages Under Peak Load Using Envelop EM Semi-supervised Learning

Nitin Khosla, Dharmendra Sharma

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

Abstract

The main objective of an expected maximization (EM) based envelop semi-supervised neural net learning approach in this paper is to develop a model for forecasting peak CPU usages under unpredictable web traffic (load conditions) in a large enterprise applications environment with several hundred live applications. This proposed approach forecasts the likelihood of extreme peak response time because of the stressed CPU due to a burst in incoming web traffic from the live IT applications and then predicts the CPU utilization under extreme load (peak) conditions. The enterprise complex IT infrastructure consists of many applications running simultaneously in real-time. Features are extracted after analyzing the CPU-load patterns of demand which are mainly hidden in the data related to key transactions of the IT applications. This method generates synthetic CPU load profiles by simulating virtual users and execute the key transactions in the test environment. This model is used to predict the excessive peak utilization under peaked CPU conditions. We have used envelope expectation maximization classifier method with forced learning, attempting to extract and analyze the parameters that maximize the likelihood of the model after marginalizing out the unknown labels. This has resulted in mitigating the risks of system failures and enabled us to manage the IT capacity within 3 days from 7 days. This model has helped in IT capacity planning and optimal usages of existing IT infrastructure with minimal risk.

Original languageEnglish
Title of host publicationAdvances in Deep Learning, Artificial Intelligence and Robotics - Proceedings of the 2nd International Conference on Deep Learning, Artificial Intelligence and Robotics, ICDLAIR 2020
EditorsLuigi Troiano, Alfredo Vaccaro, Roberto Tagliaferri, Nishtha Kesswani, Irene Díaz Rodriguez, Imene Brigui, Domenico Parente
Place of PublicationSwitzerland
PublisherSpringer
Pages25-34
Number of pages10
ISBN (Print)9783030853648
DOIs
Publication statusPublished - 2022
Event2nd International Conference on Deep Learning, Artificial Intelligence and Robotics, ICDLAIR 2020 - Virtual Online
Duration: 7 Dec 202018 Dec 2020

Publication series

NameLecture Notes in Networks and Systems
Volume249
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference2nd International Conference on Deep Learning, Artificial Intelligence and Robotics, ICDLAIR 2020
CityVirtual Online
Period7/12/2018/12/20

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