USENIX ATC '24 and OSDI '24

Li Zhang, Beijing University of Posts and Telecommunications; Zhe Fu, Tsinghua University; Boqing Shi and Xiang Li, Beijing University of Posts and Telecommunications; Rujin Lai and Chenyang Yang, vclusters; Ao Zhou, Xiao Ma, Shangguang Wang, and Mengwei Xu, Beijing University of Posts and Telecommunications

Huge energy consumption poses a significant challenge for edge clouds. In response to this, we introduce a new type of edge server, namely SoC Cluster, that orchestrates multiple low-power mobile system-on-chips (SoCs) through an on-chip network. For the first time, we have developed a concrete SoC Cluster consisting of 60 Qualcomm Snapdragon 865 SoCs housed in a 2U rack, which has been successfully commercialized and extensively deployed in edge clouds. Cloud gaming emerges as the principal workload on these deployed SoC Clusters, owing to the compatibility between mobile SoCs and native mobile games.

In this study, we aim to demystify whether the SoC Cluster can efficiently serve more generalized, typical edge workloads. Therefore, we developed a benchmark suite that employs state-of-the-art libraries for two critical edge workloads, i.e., video transcoding and deep learning inference. This suite evaluates throughput, latency, power consumption, and other application-specific metrics like video quality. Following this, we conducted a thorough measurement study and directly compared the SoC Cluster with traditional edge servers, with regards to electricity usage and monetary cost. Our results quantitatively reveal when and for which applications mobile SoCs exhibit higher energy efficiency than traditional servers, as well as their ability to proportionally scale power consumption with fluctuating incoming loads. These outcomes provide insightful implications and offer valuable direction for further refinement of the SoC Cluster to facilitate its deployment across wider edge scenarios.

https://www.usenix.org/conference/atc24/presentation/zhang-li-prototyping