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Sample Drift Correction Following 4D Confocal Time-lapse Imaging

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

Tajwar Mehmood¹, Seemab Latif¹, Nor Shahida Mohd Jamail²

¹School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan.

Peerj. Computer Science

|March 4, 2024

Summary

This summary is machine-generated.

This study introduces the LSTM Drift Detector (LSTMDD) for early concept drift detection in cloud computing. LSTMDD enhances resource utilization by outperforming other methods in cloud environments.

Keywords:

CPU usage Cloud usage trace Concept drift Drift detection Machine learning Memory usage

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Area of Science:

Cloud Computing
Machine Learning
Data Science

Background:

Concept drift poses challenges in cloud computing, impacting resource utilization.
Early detection of concept drift is crucial for maintaining optimal performance.
Existing drift detection methods may not be suitable for cloud environments.

Purpose of the Study:

To investigate concept drift in cloud computing.
To propose an effective solution for early concept drift detection.
To enhance resource utilization through improved drift detection.

Main Methods:

Utilized synthetic and real-world cloud datasets.
Developed a modified Long Short-Term Memory (LSTM) network, termed LSTM Drift Detector (LSTMDD).
Compared LSTMDD against existing drift detection techniques using prediction error.

Main Results:

LSTMDD demonstrated superior performance in detecting both gradual and sudden concept drift.
The proposed method is optimized for non-Gaussian distributed cloud environments.
LSTMDD showed improved anomaly detection capabilities.

Conclusions:

LSTM Drift Detector (LSTMDD) is a promising machine learning approach for concept drift in cloud computing.
Effective concept drift detection leads to more efficient resource allocation.
The findings support the use of tailored machine learning techniques for cloud data analysis.