What is it about?
This paper is about offloading the dependent task's workload in dynamic extreme edge computing or device-to-device environment, where both task generator and resource are mobile, devices are heterogenous and resource-constrained, and tasks are generated in a fluctuating workload.
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Why is it important?
Our model accommodates offloading and processing increasing data generated at the network's edge to vast distributed edge device resources instead of sending them to cloud or edge servers. It leads to reducing reliance on high-capacity servers and decreasing the servers' carbon footprint and maintenance costs.
Perspectives
I hope this paper encourages researchers to pay more attention to extreme edge computing.
Zahra Safavifar
University College Dublin
Read the Original
This page is a summary of: Sustainable Dependent Sub-Tasks Orchestration at Extreme Edge Computing: A Partitioning-based Deep Reinforcement Learning Approach, ACM Journal on Computing and Sustainable Societies, March 2025, ACM (Association for Computing Machinery),
DOI: 10.1145/3723037.
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