A neural net approach to predict burst of traffic under stress conditions in a complex IT environment

D Sharma, Nitin Khosla

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

Abstract

This paper investigates a model to predict burst of traffic under stress conditions in a complex environment, using neural network approach. This process predicts the likelihood of a burst of traffic in an IT infrastructure where large number of applications are running simultaneously in a real world, while analyzing the patterns (workload). These patterns are hidden in the data generated by transactions in the complex IT environment. We can generate and simulate workload patterns, extract and analyze data and then can predict burst in traffic under extreme load or stress conditions. Supervised learning is used to train the models and then used to analyze the load patterns. Traffic load prediction has become very critical and has enormous benefits in optimizing resources at low risk
Original languageEnglish
Title of host publicationInternational Conference on Deep Learning, Artificial Intelligence and Robotics (ICDLAIR 2019)
EditorsSushant Upadhyaya, Meenakshi Tripathi
PublisherSpringer
Pages1-7
Number of pages7
Publication statusPublished - 2019
EventInternational Conference on Deep Learning, Artificial Intelligence and Robotics 2019: ICDLAIR 2019 - Malaviya National Institute of Technology, Jaipur, India
Duration: 7 Dec 20198 Dec 2019
http://www.icaidlr2019.iaasse.org/home

Publication series

NameLecture Notes in Networks and Systems
PublisherSpringer
ISSN (Print)2367-3370

Conference

ConferenceInternational Conference on Deep Learning, Artificial Intelligence and Robotics 2019
CountryIndia
CityJaipur
Period7/12/198/12/19
Internet address

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