Efficient Edge Storage Management Based on Near Real-Time Forecasts

Authors: 
Ivan Lujic
Vincenzo De Maio
Ivona Brandic
Type: 
Proceedings contribution
Proceedings: 
2017 IEEE 1st International Conference on Fog and Edge Computing (ICFEC)
Publisher: 
IEEE
Pages: 
21 - 30
Year: 
2017
ISBN: 
ISBN: 978-1-5090-3047-7
Abstract: 
Nowadays, data analytics is utilized on edge based systems to perform near real-time decisions in proximity of the user. When performing near real-time decisions on the Edge, we need historical data to perform accurate data analytics. Since storage capacities on the Edge are limited, we are faced with a challenge to balance the quantity of data stored with the quality of near real-time decisions. In this paper, we present a three-layer architecture model for data storage management on the Edge including an adaptive algorithm that dynamically finds a trade-off between providing high forecast accuracy necessary for efficient real-time decisions, and minimizing the amount of data stored in the space-limited storage. We focus on time series data, typical in the context of sensor-based monitoring in IoT environments. By using the proposed approach it is possible to reduce the amount of stored data by an average 80.27% without affecting specified threshold for prediction accuracy.
TU Focus: 
Information and Communication Technology
Reference: 

I. Lujic, V. De Maio, I. Brandic:
"Efficient Edge Storage Management Based on Near Real-Time Forecasts";
in: "2017 IEEE 1st International Conference on Fog and Edge Computing (ICFEC)", IEEE, 2017, ISBN: 978-1-5090-3047-7, S. 21 - 30.

Zusätzliche Informationen

Last changed: 
18.12.2017 14:47:17
Accepted: 
Accepted
TU Id: 
264593
Invited: 
Department Focus: 
Business Informatics
Author List: 
I. Lujic, V. De Maio, I. Brandic
Abstract German: