Energy-Efficient Task Scheduling in Green Cloud Computing Using  Neuromorphic Spiking Neural Networks

Pawan Kumar Singh

Energy-Efficient Task Scheduling in Green Cloud Computing Using Neuromorphic Spiking Neural Networks

Authors

Pawan Kumar Singh

Keywords:

Neuromorphic computing, spiking neural networks, task scheduling, green cloud computing, energy efficiency, sustainable data centers

Abstract

Cloud data Centers now consume roughly 1 to 2 percent of global electricity, and the pressureto reduce that footprint has moved from a corporate sustainability concern to a hard operationalconstraint. Task scheduling sits at the heart of the problem because every placement decisionshapes how much energy the underlying servers, cooling systems, and networking fabric will draw. Traditional schedulers based on heuristics

References

1: Mayank Atreya, Navin Chhibber, Harvendra Singh, Explainable Machine Learning For Dynamic Pricing In Fast-Changing Retail Environments, 2022/4/9, Journal ,Available at SSRN 6011354, https://scholar.google.com/citations?view_op=view_citation&hl=en&user=fyViF1UAAAAJ&citation

for_view=fyViF1UAAAAJ:LkGwnXOMwfcC.

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Published

2024-11-20

How to Cite

Pawan Kumar Singh. (2024). Energy-Efficient Task Scheduling in Green Cloud Computing Using Neuromorphic Spiking Neural Networks . Journal of Computational Analysis and Applications (JoCAAA), 33(08), 9324–9337. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/5712