LSTMDD: an optimized LSTM-based drift detector for concept drift in dynamic cloud computing

LSTMDD: an optimized LSTM-based drift detector for concept drift in dynamic cloud computing

This study aims to investigate the problem of concept drift in cloud computing and emphasizes the importance of early detection for enabling optimum resource utilization and offering an effective solution. The analysis includes synthetic and real-world cloud datasets, stressing the need for appropri...

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Bibliographic Details
Main Authors: Tajwar Mehmood, Seemab Latif, Nor Shahida Mohd Jamail, Asad Malik, Rabia Latif
Format: Article
Language:English
Published: PeerJ Inc. 2024-01-01
Series:PeerJ Computer Science
Subjects:
Concept drift
Drift detection
Cloud usage trace
CPU usage
Memory usage
Machine learning
Online Access:https://peerj.com/articles/cs-1827.pdf

Internet

https://peerj.com/articles/cs-1827.pdf

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