Stochastic End-to-End Latency Modeling of the IoT-Edge-Cloud Continuum: Impact of Jitter and Traffic Variability on Deterministic Service Provisioning
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.