2017/03/11 by Robert Nishihara, Philipp Moritz, Nishihara, Robert +17 · 1 voice · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Cloud Computing and Resource Management #Data Stream Mining Techniques #Distributed #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Data Classification #Parallel #and Cluster Computing (cs.DC) #cs.AI #cs.DC #cs.LG
paper · pdf · doi:10.48550/arxiv.1703.03924
openalex publication_date 2017/03/11 · arxiv published 2017/03/11 · arxiv updated 2017/05/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Machine learning applications are increasingly deployed not only to serve predictions using static models, but also as tightly-integrated components of feedback loops involving dynamic, real-time decision making. These applications pose a new set of requirements, none of which are difficult to achieve in isolation, but the combination of which creates a challenge for existing distributed execution frameworks: computation with millisecond latency at high throughput, adaptive construction of arbitrary task graphs, and execution of heterogeneous kernels over diverse sets of resources. We assert that a new distributed execution framework is needed for such ML applications and propose a candidate approach with a proof-of-concept architecture that achieves a 63x performance improvement over a state-of-the-art execution framework for a representative application.