Large-Scale Measurements and Optimizations on Latency in Edge Clouds

The emergence of next-generation latency-critical applications places strict requirements on network latency and stability. Edge cloud, an instantiated paradigm for edge computing, is gaining more and more attention due to its benefits of low latency. In this work, we make an in-depth investigation into the network QoS, especially end-to-end latency, at both spatial and temporal dimensions on a nationwide edge computing platform. Through the measurements, we collect a multi-variable large-scale real-world dataset on latency. We then quantify how the spatial-temporal factors affect the end-to-end latency, and verify the predictability of end-to-end latency. The results reveal the limitation of centralized clouds and illustrate how could edge clouds provide low and stable latency. Our results also point out that existing edge clouds merely increase the density of servers and ignore spatial-temporal factors, so they still suffer from high latency and fluctuations. Based on a quantified latency impact factor, we have proposed several optimization strategies for edge cloud latency and validated their effectiveness. We also propose a robust prototype edge cloud model based on lessons we learn from the measurement and evaluate its performance in the production environment. Evaluation result shows that edge clouds achieve 84.1% latency reduction with 0.5 ms latency fluctuation and 73.3% QoS improvement compared with the centralized clouds.