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 language | English |
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Title of host publication | International Conference on Deep Learning, Artificial Intelligence and Robotics (ICDLAIR 2019) |
Editors | Sushant Upadhyaya, Meenakshi Tripathi |
Publisher | Springer |
Pages | 1-7 |
Number of pages | 7 |
Publication status | Published - 2019 |
Event | International Conference on Deep Learning, Artificial Intelligence and Robotics 2019: ICDLAIR 2019 - Malaviya National Institute of Technology, Jaipur, India Duration: 7 Dec 2019 → 8 Dec 2019 http://www.icaidlr2019.iaasse.org/home |
Publication series
Name | Lecture Notes in Networks and Systems |
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Publisher | Springer |
ISSN (Print) | 2367-3370 |
Conference
Conference | International Conference on Deep Learning, Artificial Intelligence and Robotics 2019 |
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Country/Territory | India |
City | Jaipur |
Period | 7/12/19 → 8/12/19 |
Internet address |