Explaining Deep Learning-Based Networked Systems

by   Zili Meng, et al.

While deep learning (DL)-based networked systems have shown great potential in various applications, a key drawback is that Deep Neural Networks (DNNs) in DL are blackboxes and nontransparent for network operators. The lack of interpretability makes DL-based networked systems challenging to operate and troubleshoot, which further prevents DL-based networked systems from deploying in practice. In this paper, we propose TranSys, a novel framework to explain DL-based networked systems for practical deployment. Transys categorizes current DL-based networked systems and introduces different explanation methods based on decision tree and hypergraph to effectively explain DL-based networked systems. TranSys can explain the DNN policies in the form of decision trees and highlight critical components based on analysis over hypergraph. We evaluate TranSys over several typical DL-based networked systems and demonstrate that Transys can provide human-readable explanations for network operators. We also present three use cases of Transys, which could (i) help network operators troubleshoot DL-based networked systems, (ii) improve the decision latency and resource consumption of DL-based networked systems by  10x on different metrics, and (iii) provide suggestions on daily operations for network operators when incidences occur.



There are no comments yet.


page 20


DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications

Unsupervised Deep Learning (DL) techniques have been widely used in vari...

Twin Systems for DeepCBR: A Menagerie of Deep Learning and Case-Based Reasoning Pairings for Explanation and Data Augmentation

Recently, it has been proposed that fruitful synergies may exist between...

How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset

Deep Learning Hard (DL-HARD) is a new annotated dataset designed to more...

DLSpec: A Deep Learning Task Exchange Specification

Deep Learning (DL) innovations are being introduced at a rapid pace. How...

Engineering AI Systems: A Research Agenda

Deploying machine-, and in particular deep-learning, (ML/DL) solutions i...

State and Topology Estimation for Unobservable Distribution Systems using Deep Neural Networks

Time-synchronized state estimation for reconfigurable distribution netwo...

Exploring the Impact of Virtualization on the Usability of the Deep Learning Applications

Deep Learning-based (DL) applications are becoming increasingly popular ...

Code Repositories


Interpreting Deep Learning-Based Networking Systems

view repo
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.