Pushing Analytics to the Edge
Goutham Kamath, Agnihotri Pavan, Maria Valero, Krishanu Sarker, Wen‐Zhan Song · 2016
Edge or Fog computing is emerging as a new computing paradigm where the data processing, networking, storage and analytics are performed closer to the devices (IoT) and applications. The edge of a network plays an important role in the IoT system. It is an optimal site for off-loading bandwidth hungry IoT data. In order to generate business value out of the large volume of data on the edge, we need decentralized machine learning (ML) algorithms. These algorithms must enable edge devices to communicate autonomously and deliver information seamlessly to the decision makers. In this paper, we present EdgeSGD a decentralized stochastic gradient descent method to solve a large linear regression problem on the edge network. The solution is applied to the seismic imaging use-case and evaluated using an edge computing testbed. The proposed algorithm avoids sending raw data to the cloud, and offers faster and balanced computation. We compare the algorithm with the existing methods such as MapReduce, DGD and EXTRA. The results show that the EdgeSGD converges faster to the optimal value and is robust towards node/link failure. Finally, we test the algorithm using real-world seismic data trace from Parkfield, CA and show that our proposed algorithm can illuminate the underlying fault region of San-Andreas.