物联网-边缘-云连续体的随机端到端延迟建模：抖动和流量可变性对确定性服务提供的影响
Stochastic End-to-End Latency Modeling of the IoT-Edge-Cloud Continuum: Impact of Jitter and Traffic Variability on Deterministic Service Provisioning
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中文总结 AI 辅助
本文针对物联网-边缘-云连续体，提出基于队列的端到端延迟模型，分析时间可变性对确定性服务提供的影响，得出不同类型服务的最优执行位置及卸载策略相关结论。
中文摘要 AI 辅助
6G将在物联网-边缘-云连续体中集成通信与计算能力，使节点能在该连续体中分配工作负载。为支持对时间敏感的服务，必须控制通信与计算延迟。时间可变性的两个关键来源是到达时间抖动和流量可变性，二者均会影响数据生成、传输和处理的时间，由此产生的波动会在整个连续体中传播，增大延迟不确定性。本文研究随机时间可变性对在连续体中支持端到端确定性服务水平能力的影响。为此，我们提出一种新颖的、基于队列的连续体端到端延迟模型，并公开发布该模型。该模型联合捕获计算与通信延迟，刻画完整的端到端延迟分布，包括尾部延迟。我们的分析表明，具有严格延迟截止期限和更大计算需求的服务对时间可变性更敏感，因此本地执行是更优选择；相比之下，具有更宽松截止期限的服务在本地或边缘执行时，尽管平均延迟和尾部延迟更高，但对时间可变性的适应性更强。在蜂窝连接良好且本地处理工作负载增加时，边缘卸载是有益的；而云执行对流量可变性更敏感，因为存在额外的通信延迟。我们的分析还显示，卸载的服务对流量可变性的敏感性高于对抖动的敏感性，原因是通信延迟更高。这些发现强调，有效的服务卸载必须同时考虑服务需求和时间可变性的来源，以保证确定性服务水平。
英文摘要
6G will integrate communication and computing capabilities in a IoT-edge-cloud continuum, enabling nodes to distribute workloads across the continuum. To support time-sensitive services, both communications and computing latencies must be controlled. Two key sources of temporal variability are arrival-time jitter and traffic variability. They can both impact the timing at which data is generated, transmitted and processed, and the resulting fluctuations can propagate throughout the continuum, increasing latency uncertainty. This paper studies the impact of stochastic temporal variability on the ability to support end-to-end deterministic service levels across the continuum. To this end, we present a novel queueing-based end-to-end latency model for the continuum, which we openly release. The model jointly captures computing and communication latency, and characterizes the complete end-to-end latency distribution, including tail latency. Our analysis shows that services with stringent latency deadlines and larger computing demands are more sensitive to temporal variabilities, making local execution the preferred option. In contrast, services with more relaxed deadlines are more resilient to temporal variabilities when executed locally or at the edge despite higher average and tail latencies. Edge offloading is beneficial under good cellular connectivity and increasing local processing workloads, whereas cloud execution is more sensitive to traffic variabilities because of the additional communication latency. Our analysis also shows that services offloaded are more sensitive to traffic variability than jitter due to higher communication latencies. These findings highlight that effective service offloading must jointly consider service requirements and sources of temporal variability to guarantee deterministic service levels.
