HeteroEdge: Taming the heterogeneity of edge computing system in social sensing

Abstract

Social sensing has emerged as a new sensing application paradigm where measurements about the physical world are collected from humans or devices on their behalf. The advent of edge computing pushes the frontier of computation, service, and data along the cloud-to-IoT continuum. The merge of these two technical trends (referred to as Social Sensing based Edge Computing or SSEC) generates a set of new research challenges. One critical issue in SSEC is the heterogeneity of the edge where the edge devices owned by human sensors often have diversified computational power, runtime environments, network interfaces, and hardware equipment. Such heterogeneity poses significant challenges in the resource management of SSEC systems. Examples include masking the pronounced heterogeneity across diverse platforms, allocating interdependent tasks with complex requirements on devices with different resources, and adapting to the dynamic and diversified context of the edge devices. In this paper, we develop a new resource management framework, HeteroEdge, to address the heterogeneity of SSEC by 1) providing a uniform interface to abstract the device details (hardware, operating system, CPU); and 2) effectively allocating the social sensing tasks to the heterogeneous edge devices. We implemented HeteroEdge on a real-world edge computing testbed that consists of heterogeneous edge devices (Jetson TX2, TK1, Raspberry Pi3, and personal computer). Evaluations based on two real-world social sensing applications show that the HeteroEdge achieved up to 42% decrease in end-to-end delay for the application and 22% more energy savings compared to the state-of-the-art baselines.