Beiträge in Tagungsbänden:
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)",
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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