The Design and Operation of CloudLab
By Dmitry Duplyakin, R. Ricci, Aleksander Maricq, Gary Wong, Jonathon Duerig, Eric Eide, L. Stoller, Mike Hibler, David Johnson, Kirk Webb and 9 more. USENIX Annual Technical Conference, 2019
672 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/05e41e129cb49cafc36810c7b062f707ada13fce
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- Peifeng Yu
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- Jacob Colleran
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Citations in the corpus
- Asynchronous Online Federated Learning for Edge Devices (2019, methodology): “f each client set as 2. We employ early stopping with a patience value of 40 global iterations on validation loss. All of the experiments are conducted with an Intel E5-2683 v3 56-core CPU at 2.00GHz [5]. 5 Experimental Results 5.1 Performance Comparison. Table 2 shows the comparison among dierent m”
- Lessons Learned from the Chameleon Testbed (2020, methodology): “Unlike traditional CS experimental systems such as Grid’5000 [1], Emulab/CloudLab [2,3], or GENI [4], which have generally been configured by technologies developed in-house, Chameleon adapted the OpenStack mainstream open-source cloud technology to provide its capabilities.”
- Asynchronous Online Federated Learning for Edge Devices with Non-IID Data (2019, methodology): “All of the experiments are conducted with two Intel E5-2660 v3 10-core CPU at 2.60GHz [42].”
- Salus: Fine-Grained GPU Sharing Primitives for Deep Learning Applications (2019, methodology): “Environment All experiments were done on a x86 64 based Intel Xeon E5-2670 machine with 2 NVIDIA Tesla P100 GPUs available in CloudLab (Duplyakin et al., 2019).”