RTSLPS: Real time server load prediction system for the ever-changing cloud computing environment-Reference-Cited by-同舟云学术

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RTSLPS: Real time server load prediction system for the ever-changing cloud computing environment

Published:2019-12 Issue: Volume: Page:
ISSN:1319-1578
Container-title:Journal of King Saud University - Computer and Information Sciences
language:en
Short-container-title:Journal of King Saud University - Computer and Information Sciences

Author:

Toumi Hajer,Brahmi Zaki,Gammoudi Mohhamed Mohsen

Publisher

Elsevier BV

Subject

General Computer Science

Reference57 articles.

1. Albert, B., Ricard, G., 2007. Learning from time-changing data with adaptive windowing. In SDM, SIAM:443-448.

2. New ensemble methods for evolving data streams;Albert,2009

3. MOA: massive online analysis;Albert;J. Mach. Learn. Res.,2010

4. Albert, B., Gianmarco, D.F.M., Jesse, R., Geoff, H., Bernhard, P., 2015. Efficient Online Evaluation of Big Data Stream Classifiers. KDD. 59-68.

5. PGNBC: Pearson Gaussian Naïve Bayes classifier for data stream classification with recurring concept drift;Babu;Intell. Data Anal.,2017

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1. Deep reinforcement learning task scheduling method based on server real-time performance;PeerJ Computer Science;2024-06-21

2. ETSA-LP: Ensemble Time-Series Approach for Load Prediction in Cloud;Computing and Informatics;2024

3. Automated Hyperparameter Tuning for Adaptive Cloud Workload Prediction;Proceedings of the IEEE/ACM 16th International Conference on Utility and Cloud Computing;2023-12-04

4. Host load prediction in cloud computing with Discrete Wavelet Transformation (DWT) and Bidirectional Gated Recurrent Unit (BiGRU) network;Computer Communications;2023-01

5. A new temporal locality-based workload prediction approach for SaaS services in a cloud environment;Journal of King Saud University - Computer and Information Sciences;2021-05

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