Cross-platform machine learning characterization for task allocation in IoT ecosystems

With the emergence of the Internet of Things (IoT) and Big Data era, many applications are expected to assimilate a large amount of data collected from environment to extract useful information. However, how heterogeneous computing devices of IoT ecosystems can execute the data processing procedures has not been clearly explored. In this paper, we propose a framework which characterizes energy and performance requirements of the data processing applications across heterogeneous devices, from a server in the cloud and a resource-constrained gateway at edge. We focus on diverse machine learning algorithms which are key procedures for handling the large amount of IoT data. We build analytic models which automatically identify the relationship between requirements and data in a statistical way. The proposed framework also considers network communication cost and increasing processing demand. We evaluate the proposed framework on two heterogenous devices, a Raspberry Pi and a commercial Intel server. We show that the identified models can accurately estimate performance and energy requirements with less than error of 4.8% for both platforms. Based on the models, we also evaluate whether the resource-constrained gateway can process the data more efficiently than the server in the cloud. The results present that the less-powerful device can achieve better energy and performance efficiency for more than 50% of machine learning algorithms.