TrustServing: A Quality Inspection Sampling Approach for Remote DNN Services

Xueyu Hou, Tao Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Deep neural networks (DNNs) are being applied to various areas such as computer vision, autonomous vehicles, and healthcare, etc. However, DNNs are notorious for their high computational complexity and cannot be executed efficiently on resource constrained Internet of Things (IoT) devices. Various solutions have been proposed to handle the high computational complexity of DNNs. Offloading computing tasks of DNNs from IoT devices to cloud/edge servers is one of the most popular and promising solutions. While such remote DNN services provided by servers largely reduce computing tasks on IoT devices, it is challenging for IoT devices to inspect whether the quality of the service meets their service level objectives (SLO) or not. In this paper, we address this problem and propose a novel approach named QIS (quality inspection sampling) that can efficiently inspect the quality of the remote DNN services for IoT devices. To realize QIS, we design a new ID-generation method to generate data (IDs) that can identify the serving DNN models on edge servers. QIS inserts the IDs into the input data stream and implements sampling inspection on SLO violations. The experiment results show that the QIS approach can reliably inspect, with a nearly 100% success rate, the service qualtiy of remote DNN services when the SLA level is 99.9% or lower at the cost of only up to 0.5% overhead.

Original languageEnglish (US)
Title of host publication2020 17th IEEE International Conference on Sensing, Communication and Networking, SECON 2020
PublisherIEEE Computer Society
ISBN (Electronic)9781728166308
DOIs
StatePublished - Jun 2020
Externally publishedYes
Event17th IEEE International Conference on Sensing, Communication and Networking, SECON 2020 - Virtual, Online, Italy
Duration: Jun 22 2020Jun 25 2020

Publication series

NameAnnual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops
ISSN (Print)2155-5486
ISSN (Electronic)2155-5494

Conference

Conference17th IEEE International Conference on Sensing, Communication and Networking, SECON 2020
Country/TerritoryItaly
CityVirtual, Online
Period6/22/206/25/20

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Electrical and Electronic Engineering

Keywords

  • AIoT
  • Cloud Computing
  • Edge Computing
  • MLaaS

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