- Aurick Qiao, Abutalib Aghayev, Weiren Yu, Haoyang Chen, Qirong Ho, Garth A. Gibson, E. Xing. 2018. Litz: Elastic Framework for High-Performance Distributed Machine Learning. Abstract: Machine Learning (ML) is an increasingly popular application in the cloud and data-center, inspiring new algorithmic and systems techniques that leverage unique properties of ML applications to improve their distributed performance by orders of magnitude. However, applications built using these techniques tend to be static, unable to elastically adapt to the changing resource availability that is characteristic of multi-tenant environments. Existing distributed frameworks are either inelastic, or offer programming models which are incompatible with the techniques employed by high-performance ML applications. 
