Study on Network Importance for ML End Application Robustness

Sergei Chuprov, Leon Reznik, Garegin Grigoryan

Abstract: In this paper, we investigate the robustness of the ML end application performance to network Quality of Service (QoS) degradation, and the ways to improve it. We introduce a novel system approach to define the Machine Learning (ML) with Integrated Networks (MLINs) and describe how ML end performance can be employed to adjust network hyperparameters in order to prevent system Data Quality decrease. We investigate the interrelations between the network QoS degradation during the data transmission and ML image classification performance. We demonstrate how the studied interrelationships can be employed to produce recommendations on network adjustment in order to improve MLIN robustness. In particular, we propose an example of MLIN feedback system design that employs ML end performance as the major indicator to produce recommendations on network hyperparameters adjustment aimed at improving MLIN robustness.