CoSMO framework optimizes edge-cloud task execution using reinforcement learning

Researchers have developed CoSMO, a novel reinforcement learning framework designed to optimize task execution in edge-cloud computing environments. This system addresses the challenge of partial observability by coordinating semantic status management and selective task offloading. CoSMO utilizes a recurrent semi-Markov double deep Q-network at the service node and a task-terminal off-policy value-learning agent at the edge node to improve decision accuracy and on-time task completion rates. AI

IMPACT This research could lead to more efficient resource allocation and task management in distributed computing systems.

RANK_REASON The item is an academic paper detailing a new framework for edge-cloud computing. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.MA (Multiagent) →

• arXiv
• Cloud Node
• CoSMO
• Edge-node touchless authentication architecture
• Markov
• Q-network
• reinforcement learning

• paper
• infra

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