Research on Resource Demand Prediction and Optimal Allocation Method in Cloud Computing Environment

Sun, Yuehong; Zhou, Huanjiao Jenny

doi:10.1007/978-981-16-3180-1_8

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…Xu Dayu et al [20] addressed the challenge of predicting resource demands in cloud environments and proposed short-term load forecasting method employed feature extraction to capture relevant information from dynamic cloud resources, while the long-term load forecasting method integrated multiple forecasting algorithms and leveraged generalized fuzzy set theory to aggregate their predictions effectively. The hybrid technique for forecasting host load in cloud computing was introduced by Chen et al [21].…”

Section: Related Workmentioning

confidence: 99%

A fine tune hyper parameter Gradient Boosting model for CPU utilization prediction in cloud

Khurana,

Sharma,

Sharma

2023

Preprint

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CPU utilization prediction is key factor for efficient resource management and capacity planning in cloud computing environments. By accurately predicting utilization patterns, resource managers can dynamically distribute workloads to ensure optimal utilization of resources. The load can be equally distributed among virtual machines, leading to a reduction in VM migration and overhead time. This optimization significantly improves the overall performance of the cloud. This proactive approach enables efficient resource usage, minimizing the risk of bottlenecks and maximizing overall system performance. In this paper a Gradient Boosting model with hyper parameter tuning based upon grid search (GBHT) is proposed to enhance CPU utilization prediction. Multiple weak learners are combined in the proposed model to produce a powerful prediction model. and hyperparameters tuning is used to enhance its performance as well as predictive accuracy. Different machine learning and deep learning models are examined side by side. The results clearly demonstrate that the proposed GBHT model significantly contribute superior performance then the traditional machine learning models (SVM, KNN, Random Forest, Gradient Boost), deep learning models (LSTM, RNN, CNN), time series model (Facebook Prophet) and as well as the hybrid models, combining LSTM with Gradient Boost and Gradient Boost with SVM. The proposed model demonstrates superior performance compared to the other models, achieving the lowest MAPE of 0.01% and high accuracy with an R2 score of 1.00.

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Section: Related Workmentioning

confidence: 99%

A fine tune hyper parameter Gradient Boosting model for CPU utilization prediction in cloud

Khurana,

Sharma,

Sharma

2023

Preprint

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“…By integrating the self-organizing capabilities of GMDH with the evolutionary algorithm, the PSR and EA-GMDH method enhanced prediction accuracy and the ability to handle complex data patterns. Xu Dayu et al [20] addressed the challenge of predicting resource demands in cloud environments and proposed short-term load forecasting method employed feature extraction to capture relevant information from dynamic cloud resources, while the long-term load forecasting method integrated multiple forecasting algorithms and leveraged generalized fuzzy set theory to aggregate their predictions effectively. The hybrid technique for forecasting host load in cloud computing was introduced by Chen et al [21].…”

Section: Related Workmentioning

confidence: 99%

A fine tune hyper parameter Gradient Boosting model for CPU utilization prediction in cloud environment

savita,

gaurav,

Bhawna

2023

Preprint

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Research on Resource Demand Prediction and Optimal Allocation Method in Cloud Computing Environment

Cited by 2 publications

References 3 publications

A fine tune hyper parameter Gradient Boosting model for CPU utilization prediction in cloud

A fine tune hyper parameter Gradient Boosting model for CPU utilization prediction in cloud

A fine tune hyper parameter Gradient Boosting model for CPU utilization prediction in cloud environment

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