Mehmood Tajwar, Latif Seemab, Jamail Nor Shahida Mohd, Malik Asad, Latif Rabia
School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan.
Artificial Intelligence and Data Analytics Laboratory, College of Computer and Information Sciences (CCIS), Prince Sultan University, Riyadh, Saudi Arabia.
PeerJ Comput Sci. 2024 Jan 31;10:e1827. doi: 10.7717/peerj-cs.1827. eCollection 2024.
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 appropriate drift detectors tailored to the cloud domain. A modified version of Long Short-Term Memory (LSTM) called the LSTM Drift Detector (LSTMDD) is proposed and compared with other top drift detection techniques using prediction error as the primary evaluation metric. LSTMDD is optimized to improve performance in detecting anomalies in non-Gaussian distributed cloud environments. The experiments show that LSTMDD outperforms other methods for gradual and sudden drift in the cloud domain. The findings suggest that machine learning techniques such as LSTMDD could be a promising approach to addressing the problem of concept drift in cloud computing, leading to more efficient resource allocation and improved performance.
本研究旨在调查云计算中的概念漂移问题，并强调早期检测对于实现最佳资源利用和提供有效解决方案的重要性。分析包括合成云和真实世界的云数据集，强调需要针对云领域定制合适的漂移检测器。提出了一种名为长短期记忆漂移检测器（LSTMDD）的长短期记忆（LSTM）修改版本，并将其与其他顶级漂移检测技术进行比较，使用预测误差作为主要评估指标。LSTMDD经过优化，以提高在非高斯分布云环境中检测异常的性能。实验表明，LSTMDD在云领域的渐变和突变漂移方面优于其他方法。研究结果表明，诸如LSTMDD之类的机器学习技术可能是解决云计算中概念漂移问题的一种有前途的方法，从而实现更高效的资源分配并提高性能。
