Understanding Serverless Inference in Mobile-Edge Networks: A Benchmark Approach

Abstract

Although the emerging serverless paradigm has the potential to become a dominant way of deploying cloud-service tasks across millions of mobile and IoT devices, the overhead characteristics of executing these tasks on such a volume of mobile devices remain largely unclear. To address this issue, this paper conducts a deep analysis based on the OpenFaaS platform—a popular open-source serverless platform for mobile edge environments—to investigate the overhead of performing deep learning inference tasks on mobile devices. To thoroughly evaluate the inference overhead, we develop a performance benchmark, named ESBench, whereby a set of comprehensive experiments are conducted with respect to a bunch of simulated mobile devices associated with an edge cluster. Our investigation reveals that the performance of deep learning inference tasks is significantly influenced by the model size and resource contention in mobile devices, leading to up to $3\times$ degradation in performance. Moreover, we observe that the network environment can negatively impact the performance of mobile inference, increasing the CPU overhead under poor network conditions. Based on our findings, we further propose some recommendations for designing efficient serverless platforms and resource management strategies as well as for deploying serverless computing in the mobile edge environment.