Optimized Hierarchical Tree Deep Convolutional Neural Network of a Tree-Based Workload Prediction Scheme for Enhancing Power Efficiency in Cloud Computing

Thirumalai Selvan Chenni Chetty (), Vadim Bolshev (), Siva Shankar Subramanian, Tulika Chakrabarti, Prasun Chakrabarti, Vladimir Panchenko, Igor Yudaev and Yuliia Daus
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Thirumalai Selvan Chenni Chetty: Department of Computer Science and Engineering, Gitam School of Technology, Gitam University, Bengaluru 561203, Karnataka, India
Vadim Bolshev: Laboratory of Power Supply and Heat Supply, Federal Scientific Agroengineering Center VIM, 109428 Moscow, Russia
Siva Shankar Subramanian: Department of Computer Science and Engineering, KG Reddy College of Engineering and Technology, Chilkur Village, Moinabad Mandal, RR District, Hyderabad 501504, Telangana, India
Tulika Chakrabarti: Department of Chemistry, Sir Padampat Singhania University, Udaipur 313601, Rajasthan, India
Prasun Chakrabarti: Department of Computer Science and Engineering, lTM SLS Baroda University, Vadodara 391510, Gujarat, India
Vladimir Panchenko: Department of Theoretical and Applied Mechanics, Russian University of Transport, 127994 Moscow, Russia
Igor Yudaev: Energy Department, Kuban State Agrarian University, 350044 Krasnodar, Russia
Yuliia Daus: Energy Department, Kuban State Agrarian University, 350044 Krasnodar, Russia

Energies, 2023, vol. 16, issue 6, 1-16

Abstract: Workload prediction is essential in cloud data centers (CDCs) for establishing scalability and resource elasticity. However, the workload prediction accuracy in the cloud data center could be better due to noise, redundancy, and low performance for workload prediction. This paper designs a hierarchical tree-based deep convolutional neural network (T-CNN) model with sheep flock optimization (SFO) to enhance CDCs’ power efficiency and workload prediction. The kernel method is used to preprocess historical information from the CDCs. Additionally, T-CNN model weight parameters are optimized using SFO. The suggested TCNN-SFO technology has successfully reduced excessive power consumption while correctly forecasting the incoming demand. Further, the proposed model is assessed using two benchmark datasets: Saskatchewan HTTP traces and NASA. The developed model is executed in a Java tool. Therefore, associated with existing methods, the developed technique has achieved higher accuracy of 20.75%, 19.06%, 29.09%, 23.8%, and 20.5%, as well as lower energy consumption of 20.84%, 18.03%, 28.64%, 30.72%, and 33.74% when validating the Saskatchewan HTTP traces dataset. It has also achieved higher accuracy of 32.95%, 12.05%, 32.65%, and 26.54%.

Keywords: cloud data center; cloud computing; convolutional neural network; sheep flock optimization; workload prediction; kernel correlation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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