通过多路径冗余通信，提升云机器人任务延迟可靠性。
FogROS2-PLR: Probabilistic Latency-Reliability For Cloud Robotics
- 用多个独立网络接口向多个云端服务器并发请求，优先采用首个返回结果。
- 实测在双5G环境下，99百分位延迟降低3.7倍，平均延迟减少36%。
- 适合对实时性要求高的云机器人场景，如自动驾驶与人机追踪。
云机器人将计算密集型任务卸载至云端以提升性能、降低成本并便于管理。然而，网络与云计算基础设施难以保证可靠的时序服务，因服务质量（QoS）波动。本文提出一个不可能三角定理：延迟可靠性、单个服务器和通用硬件三者不可兼得。该定理表明，采用具有不相关故障的冗余服务器可指数级降低任务超时概率。为此，我们提出FogROS2-PLR，利用机器人上多个独立网络接口，向多个云端服务器并发发送请求，并采用最先返回的结果。设计了路由机制以发现、连接并经由非默认接口进行通信。该系统优化接口与服务器的选配策略，以最小化超时概率。我们在两个5G服务商环境下开展联网驾驶实验，验证即使其中一个服务商出现覆盖下降或基站切换，仍能保持平滑的服务质量。采用99百分位（P99）延迟评估长尾异常延迟。实验显示，与仅使用单一服务商相比，FogROS2-PLR将P99延迟改善达3.7倍。在真实Stretch 3机器人上部署，执行室内人追踪任务，在全覆5G与Wi-Fi环境下，平均延迟降低36%，P99延迟降低33%。
原文摘要 · Abstract (English)
Cloud robotics enables robots to offload computationally intensive tasks to cloud servers for performance, cost, and ease of management. However, the network and cloud computing infrastructure are not designed for reliable timing guarantees, due to fluctuating Quality-of-Service (QoS). In this work, we formulate an impossibility triangle theorem for: Latency reliability, Singleton server, and Commodity hardware. The LSC theorem suggests that providing replicated servers with uncorrelated failures can exponentially reduce the probability of missing a deadline. We present FogROS2-Probabilistic Latency Reliability (PLR) that uses multiple independent network interfaces to send requests to replicated cloud servers and uses the first response back. We design routing mechanisms to discover, connect, and route through non-default network interfaces on robots. FogROS2-PLR optimizes the selection of interfaces to servers to minimize the probability of missing a deadline. We conduct a cloud-connected driving experiment with two 5G service providers, demonstrating FogROS2-PLR effectively provides smooth service quality even if one of the service providers experiences low coverage and base station handover. We use 99 Percentile (P99) latency to evaluate anomalous long-tail latency behavior. In one experiment, FogROS2-PLR improves P99 latency by up to 3.7x compared to using one service provider. We deploy FogROS2-PLR on a physical Stretch 3 robot performing an indoor human-tracking task. Even in a fully covered Wi-Fi and 5G environment, FogROS2-PLR improves the responsiveness of the robot reducing mean latency by 36% and P99 latency by 33%.
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