λDNN: Achieving Predictable Distributed DNN Training With Serverless Architectures

Serverless computing is becoming a promising paradigm for Distributed Deep Neural Network (DDNN) training in the cloud, as it allows users to decompose complex model training into a number of functions without managing virtual machines or servers. Though provided with a simpler resource interface (i...

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Veröffentlicht in:	IEEE transactions on computers 2022-02, Vol.71 (2), p.450-463
Hauptverfasser:	Xu, Fei, Qin, Yiling, Chen, Li, Zhou, Zhi, Liu, Fangming
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Bandwidth Central Processing Unit Cloud computing Computational modeling Distributed DNN training Empirical analysis function resource provisioning Performance prediction predictable performance Provisioning Resource allocation serverless computing Servers Throughput Training Virtual environments
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