A large-scale holistic measurement of crowdsourced edge cloud platform

Edge clouds have become a de-facto paradigm to deliver low and stable networks to delay-critical applications such as Web services and AR/VR. A unique form of edge clouds is those crowdsourced from third parties, e.g., idle PCs or workstations. Such crowdsourced edge platforms can better sink computations closer to users, reduce the purchase cost, and eliminates the carbon generated during manufacturing. Yet, they also face the challenge of out-of-control hardware, e.g., a server dropping in/out anytime. In this paper, we perform the first-of-its-kind measurement of Quality of Service (QoS) for a large-scale crowdsourced edge platform, which covers over 10,000 edge servers, 100,000 users and 10,000,000 user requests. The measurement takes a holistic QoS view: First, we look at how much hardware resources are provided by edge servers, how much time they are available for service deployment, how geographic distance affects network performance, and what are the major abnormal behaviors. Second, we analyze the factors affecting service stability and quantify the resource utilization pattern of containerized services hosted on those edge servers. Third, we investigate the spatial and temporal features of user requests handled by the platform. Many useful and somehow surprising findings are obtained through the above measurements. We also derive insightful implications that could help edge platforms and edge applications to better deliver their services to users.