Appropriate Graph-Algorithm Selection for Edge Devices Using Machine Learning

As a new information technology, edge computing has attracted much attention in recent years. Edge computing collects, stores, and processes data on the edge devices, such as smartphones and sensors. Edge devices can process data in real time by reducing communication time with central servers. Therefore, various algorithms for preprocessing should be executed on edge devices to provide services rapidly. Graph algorithms are one of such candidates, because graph data play an important role in representing a variety of information around us such as maps, SNS, and web structures, which is one of data collected and processed on edge devices. However, since execution time of graph algorithms varies greatly depending on the graph data, the preprocessing on edge devices may take a long time. Therefore, it is necessary to select an appropriate algorithm and process data for edge devices as fast as possible. This paper proposes a method to select the appropriate graph algorithm on edge devices. By using machine learning that takes features of graph data as input, the performances of graph algorithms, such as the execution times, are predicted. Then, the proposed method selects a suitable algorithm for requests of edge users. The evaluation result demonstrates that the proposed method can select the appropriate algorithm from the several algorithms depending on the characteristics of graph data.