Staleness Control for Edge Data Analytics

Authors: 
Atakan Aral
Melike Erol-Kantarci
Ivona Brandic
Type: 
Speech with proceedings
Proceedings: 
SIGMETRICS '20: Abstracts of the 2020 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems, June 2020
Publisher: 
Association for Computing Machinery, New York, NY, United States
Pages: 
19 - 20
ISBN: 
ISBN: 978-1-4503-7985-4
Year: 
2020
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.
TU Focus: 
Information and Communication Technology
Reference: 

A. Aral, M. Erol-Kantarci, I. Brandic:
"Staleness Control for Edge Data Analytics";
Vortrag: SIGMETRICS 2020 - ACM International Conference on Measurement and Modeling of Computer Systems, Boston, Massachusetts, USA (Online Conference); 08.06.2020 - 12.06.2020; in: "SIGMETRICS '20: Abstracts of the 2020 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems, June 2020", Association for Computing Machinery, New York, NY, United States, (2020), ISBN: 978-1-4503-7985-4; S. 19 - 20.

Zusätzliche Informationen

Last changed: 
09.01.2021 03:36:24
TU Id: 
294083
Accepted: 
Accepted
Invited: 
Department Focus: 
Business Informatics
Abstract German: 
Author List: 
A. Aral, M. Erol-Kantarci, I. Brandic