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Energy-aware inference offloading for DNN-driven applications in mobile edge clouds

Xu, Zichuan, Zhao, Liqian, Liang, Weifa, Rana, Omer ORCID:, Zhou, Pan, Xia, Qiufen, Xu, Wenzheng and Wu, Guowei 2021. Energy-aware inference offloading for DNN-driven applications in mobile edge clouds. IEEE Transactions on Parallel and Distributed Systems 32 (4) 10.1109/TPDS.2020.3032443

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Deep Neural Networks (DNNs) have been successfully used in a number of application areas. As the number of layers and neurons in DNNs increases rapidly, significant computational resources are needed to execute a DNN. This ever-increasing resource demand is currently met by large-scale data centers with state-of-the-art GPUs. However, increasing availability of mobile edge computing and 5G technologies provide new possibilities for DNN-driven AI applications, especially where these application make use of data sets that are distributed in different locations. One fundamental process of a DNN-driven application in mobile edge clouds is the adoption of ``inferencing'' - the process of executing a pre-trained DNN based on newly generated data from mobile devices. We investigate offloading DNN inference requests in a 5G-enabled mobile edge cloud (MEC), to admit as many inference requests as possible. We propose exact and approximate solutions for the problem. We also consider dynamic task offloading for inference requests, and devise an online algorithm that can be adapted in real time. The proposed algorithms are evaluated through large-scale simulations and using a real world test-bed. The experimental results demonstrate that the empirical performance of the proposed algorithms outperform their theoretical counterparts and other similar heuristics reported in literature.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
ISSN: 1045-9219
Last Modified: 09 Nov 2022 09:29

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