Measuring the Edge: A Performance Evaluation of Edge Offloading

When the demand for computationally-intensive applications exceeds the capabilities of a single device, computation offloading can help to leverage remote resources. Currently, most systems only use cloud and grid resources as offloading targets, ignoring the vast amount of end-user devices that might contribute with their idle CPU cycles. Even though these edge devices have great potential, two main reasons make their usage more complicated: First, the devices are highly heterogeneous and unreliable. Second, numerous middleboxes on the Internet impose barriers, making communication between end-user devices difficult. Thus, in this paper, we overcome these two obstacles and propose an offloading system composed not only of cloud and grid resources but also of geographically distributed and heterogeneous end-user devices. We deploy this system in realistic environments and evaluate its performance under real-world conditions. Our results indicate that offloading to edge devices owned by end users can compete with cloud and grid offloading while adding minimal communication overhead. Our empirical findings support the hypothesis that edge computing can be a cost-efficient alternative to traditional offloading systems.