Octopus: SLO-Aware Progressive Inference Serving via Deep Reinforcement Learning in Multi-tenant Edge Cluster

Author:

Zhang ZiyangORCID,Zhao Yang,Liu Jie

Publisher

Springer Nature Switzerland

Reference25 articles.

1. Choi, S., Lee, S., Kim, Y., Park, J., Kwon, Y., Huh, J.: Serving heterogeneous machine learning models on $$\{$$Multi-GPU$$\}$$ servers with $$\{$$Spatio-Temporal$$\}$$ sharing. In: 2022 USENIX Annual Technical Conference (USENIX ATC 2022), pp. 199–216 (2022)

2. Christodoulou, P.: Soft actor-critic for discrete action settings. arXiv preprint arXiv:1910.07207 (2019)

3. Dong, F., et al.: Multi-exit DNN inference acceleration based on multi-dimensional optimization for edge intelligence. IEEE Trans. Mob. Comput. (2022)

4. Faggioli, D., Trimarchi, M., Checconi, F., Bertogna, M., Mancina, A.: An implementation of the earliest deadline first algorithm in linux. In: Proceedings of the 2009 ACM Symposium on Applied Computing, pp. 1984–1989 (2009)

5. Gujarati, A., et al.: Serving $$\{$$DNNs$$\}$$ like clockwork: performance predictability from the bottom up. In: 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2020), pp. 443–462 (2020)

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