Adaptive workload adjustment for cyber-physical systems using deep reinforcement learning

Shikang Xu; Israel Koren; C.M. Krishna

doi:10.1016/j.suscom.2021.100525

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JOURNAL ARTICLE

Adaptive workload adjustment for cyber-physical systems using deep reinforcement learning

Shikang Xu Israel Koren C.M. Krishna

Year: 2021 Journal: Sustainable Computing Informatics and Systems Vol: 30 Pages: 100525-100525 Publisher: Elsevier BV

DOI: 10.1016/j.suscom.2021.100525

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Keywords:

Computer science Workload Energy consumption Redundancy (engineering) Cyber-physical system Frequency scaling Reinforcement learning Embedded system Simulation Artificial intelligence Electrical engineering

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Topics

Green IT and Sustainability

Physical Sciences → Engineering → Electrical and Electronic Engineering

Real-Time Systems Scheduling

Physical Sciences → Computer Science → Hardware and Architecture

Parallel Computing and Optimization Techniques

Physical Sciences → Computer Science → Hardware and Architecture

Adaptive workload adjustment for cyber-physical systems using deep reinforcement learning

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