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Green Computing-Based Digital Waste Management and Resource Allocation for Distributed Fog Data Centers

Green Computing-Based Digital Waste Management and Resource Allocation for Distributed Fog Data Centers

N. Manikandan, D. Vinod, R. Anto Arockia Rosaline, P. Nancy, G. Premalatha
Copyright: © 2024 |Pages: 18
ISBN13: 9798369315521|ISBN13 Softcover: 9798369345474|EISBN13: 9798369315538
DOI: 10.4018/979-8-3693-1552-1.ch011
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MLA

Manikandan, N., et al. "Green Computing-Based Digital Waste Management and Resource Allocation for Distributed Fog Data Centers." Computational Intelligence for Green Cloud Computing and Digital Waste Management, edited by K. Dinesh Kumar, et al., IGI Global, 2024, pp. 209-226. https://doi.org/10.4018/979-8-3693-1552-1.ch011

APA

Manikandan, N., Vinod, D., Anto Arockia Rosaline, R., Nancy, P., & Premalatha, G. (2024). Green Computing-Based Digital Waste Management and Resource Allocation for Distributed Fog Data Centers. In K. Kumar, V. Varadarajan, N. Nasser, & R. Poluru (Eds.), Computational Intelligence for Green Cloud Computing and Digital Waste Management (pp. 209-226). IGI Global. https://doi.org/10.4018/979-8-3693-1552-1.ch011

Chicago

Manikandan, N., et al. "Green Computing-Based Digital Waste Management and Resource Allocation for Distributed Fog Data Centers." In Computational Intelligence for Green Cloud Computing and Digital Waste Management, edited by K. Dinesh Kumar, et al., 209-226. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-1552-1.ch011

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Abstract

The term “green computing” describes the efficient use of resources in computing and IT/IS infrastructure. This study suggests a unique method for dispersed fog data centres' work scheduling and resource allocation based on digital waste management. Here, the bandwidth differential preemption evolution moving average method (BDPEMA) is used to control the network's digital waste while allocating resources. Reinforcement adversarial hierarchical group multi-objective cuckoo optimisation (RAHMCO) is used to schedule network tasks. In terms of resource sharing rate, energy efficiency, reaction time, quality of service, and makespan, experimental study is conducted. The proposed approaches have been evaluated in a simulated cloud environment. The proposed method outperformed the current rules when QoS features were considered. The proposed technique attained QoS of 66%, energy efficiency of 96%, resource sharing of 88%, response time of 45%, and makespan of 61%.

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