Deep Reinforcement Learning Frameworks for Dynamic Load Balancing  in Cloud-Edge Environments

Shashank Gangadhar Bhagat

Deep Reinforcement Learning Frameworks for Dynamic Load Balancing in Cloud-Edge Environments

Authors

Shashank Gangadhar Bhagat

Keywords:

Deep reinforcement learning, load balancing, cloud-edge computing, task offloading, latency optimization, dynamic workloads

Abstract

Cloud-edge computing has emerged as the default architecture for latency-sensitiveapplications ranging from autonomous vehicles and industrial IoT to real-time video analytics.In this setting, workloads flow across a hierarchy of edge devices, edge servers, and cloud data centres, and the load balancing decisions made at each layer directly

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

2024-01-20

How to Cite

Shashank Gangadhar Bhagat. (2024). Deep Reinforcement Learning Frameworks for Dynamic Load Balancing in Cloud-Edge Environments. Journal of Computational Analysis and Applications (JoCAAA), 32(1), 1445–1455. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5688