Efficient resource utilization using multi-step-ahead workload prediction technique in cloud

Banerjee, Sounak; Roy, Sarbani; Khatua, Sunirmal

doi:10.1007/s11227-021-03701-y

Cited by 17 publications

(3 citation statements)

References 35 publications

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“…Banerjee et al 25 The method as multistep-ahead workload prediction based on Machine learning methods.…”

Section: Methods Advantage Disadvantagementioning

confidence: 99%

“…Banerjee et al 25 have presented the method as multistep‐ahead workload prediction based on machine learning methods. This model was allocated the resources using the prediction model, which allows the resources to use more efficient manner and for reducing the energy consumption.…”

Section: Related Workmentioning

confidence: 99%

“…Hence, the existing method does not accurately predict the work load and energy efficiency of the cloud data center is high. [23][24][25][26][27][28][29] To overcome these issues, the workload prediction model is developed in this article that has effectively forecast the workload and enhances the energy efficiency in cloud data center.…”

mentioning

confidence: 99%

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COSCO2: AI‐augmented evolutionary algorithm based workload prediction framework for sustainable cloud data centers

Karthikeyan¹,

Balamurugan²,

Cyriac

et al. 2022

Trans Emerging Tel Tech

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Workload prediction is the necessary factor in the cloud data center for maintaining the elasticity and scalability of resources. However, the accuracy of workload prediction is very low, because of redundancy, noise, and low accuracy for workload prediction in cloud data center. Therefore, in this article, a tree hierarchical deep convolutional neural network (T-CNN) optimized with sheep flock optimization algorithm based work load prediction is proposed for sustainable cloud data centers. Initially, the historical data from the cloud data center is preprocessed using kernel correlation method. The proposed T-CNN approach is used for workload prediction in dynamic cloud environment. The weight parameters of the T-CNN model are optimized by sheep flock optimization algorithm. The proposed COSCO2 method has accurately predicts the upcoming workload and reduces extravagant power consumption at cloud data centers. The proposed approach is evaluated utilizing two benchmark datasets:(i) NASA, (ii) Saskatchewan HTTP traces. The simulation of this model is implemented in java tool and the parameters are calculated. From the simulation, the proposed method attains 20.

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“…Banerjee et al 25 The method as multistep-ahead workload prediction based on Machine learning methods.…”

Section: Methods Advantage Disadvantagementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%