Staleness control for edge data analytics

Staleness control for edge data analytics

Abstract

A new generation of cyber-physical systems has emerged with a large number of devices that continuously generate and consume massive amounts of data in a distributed and mobile manner. Accurate and near real-time decisions based on such streaming data are in high demand in many areas of optimization for such systems. Edge data analytics bring processing power in the proximity of data sources, reduce the network delay for data transmission, allow large-scale distributed training, and consequently help meeting real-time requirements. Nevertheless, the multiplicity of data sources leads to multiple distributed machine learning models that may suffer from sub-optimal performance due to the inconsistency in their states. In this work, we tackle the insularity, concept drift, and connectivity issues in edge data analytics to minimize its accuracy handicap without losing its timeliness benefits. Thus, we propose an efficient model synchronization mechanism for distributed and stateful data analytics. Staleness Control for Edge Data Analytics (SCEDA) ensures the high adaptability of synchronization frequency in the face of an unpredictable environment by addressing the trade-off between the generality and timeliness of the model.

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Authors
  • Aral, Atakan
  • Erol-Kantarci, Melike
  • Brandić, Ivona
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Shortfacts
Category
Paper in Conference Proceedings or in Workshop Proceedings (Paper)
Event Title
2020 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems
Divisions
Scientific Computing
Subjects
Datenverarbeitungsmanagement
Kuenstliche Intelligenz
Theoretische Informatik
Parallele Datenverarbeitung
Systemarchitektur Allgemeines
Event Location
Boston, USA
Event Type
Conference
Event Dates
8-12 Jun 2020
Series Name
SIGMETRICS '20: Abstracts of the 2020 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems
ISSN/ISBN
978-1-4503-7985-4
Page Range
pp. 19-20
Date
June 2020
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