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Online Learning for Orchestration of Inference in Multi-user End-edge-cloud Networks by Sina Shahhosseini; Dongjoo Seo; Anil Kanduri; Tianyi Hu; Sung-Soo Lim; Bryan Donyanavard; Amir M. Rahmani; Nikil Dutt is a Computer Science article available to read on EtoBox.

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Deep-learning-based intelligent services have become prevalent in cyber-physical applications, including smart cities and health-care. Deploying deep-learning-based intelligence near the end-user enhances privacy protection, responsiveness, and reliability. Resource-constrained end-devices must be carefully managed to meet the latency and energy requirements of computationally intensive deep learning services. Collaborative end-edge-cloud computing for deep learning provides a range of performance and efficiency that can address application requirements through computation offloading. The decision to offload computation is a communication-computation co-optimization problem that varies with both system parameters (e.g., network condition) and workload characteristics (e.g., inputs). However, deep learning model optimization provides another source of tradeoff between latency and model accuracy. An end-to-end decision-making solution that considers such computation-communication problem is required to synergistically find the optimal offloading policy and model for deep learning services. To this end, we propose a reinforcement-learning-based computation offloading solution that lea

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Author

Sina Shahhosseini; Dongjoo Seo; Anil Kanduri; Tianyi Hu; Sung-Soo Lim; Bryan Donyanavard; Amir M. Rahmani; Nikil Dutt

Publisher

ACM

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)