Consistency of the Fittest: Towards Dynamic Staleness Control for Edge Data Analytics

  • Atakan AralEmail author
  • Ivona Brandic
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11339)


A critical challenge for data stream processing at the edge of the network is the consistency of the machine learning models in distributed worker nodes. Especially in the case of non-stationary streams, which exhibit high degree of data set shift, mismanagement of models poses the risks of suboptimal accuracy due to staleness and ignored data. In this work, we analyze model consistency challenges of distributed online machine learning scenario and present preliminary solutions for synchronizing model updates. Additionally, we propose metrics for measuring the level and speed of data set shift.


Edge computing Data analytics Consistency Staleness 



The work described in this paper has been funded through the Haley project (Holistic Energy Efficient Hybrid Clouds) as part of the TU Vienna Distinguished Young Scientist Award 2011 and Rucon project (Runtime Control in Multi Clouds), FWF Y 904 START-Programm 2015.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Information Systems EngineeringVienna University of TechnologyViennaAustria

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