Journal
2017 IEEE 1ST INTERNATIONAL CONFERENCE ON FOG AND EDGE COMPUTING (ICFEC)
Volume -, Issue -, Pages 21-30Publisher
IEEE
DOI: 10.1109/ICFEC.2017.9
Keywords
edge systems; edge storage management; forecast accuracy; real-time decision
Funding
- Rucon project (Runtime Control in Multi Clouds), FWF Y 904 START-Programm
- Haley project (Holistic Energy Efficient Hybrid Clouds) as part of the TU Vienna Distinguished Young Scientist Award
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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.
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