End-to-End Delay Bound for VR Services in 6G Terahertz Networks with Heterogeneous Traffic and Different Scheduling Policies

The emerging sixth-generation networks have to provide effective support to a wide plethora of novel disruptive heterogeneous applications. This paper models the probabilistic end-to-end delay bound for the virtual reality services in the presence of heterogeneous traffic flows by resorting to the stochastic network calculus principles and exploiting the martingale envelopes. The paper presents the network performance analysis under the assumption of different scheduling policies, considering both the earliest deadline first and the first-in-first-out queue discipline. Furthermore, differently from previous literature, the probabilistic per-flow bounds have been formulated taking into account a number of traffic flows greater than two, which results in a theoretical analysis that is remarkably more complex than the case in which only two concurrent flows are considered. Finally, the validity of the theoretical bounds have been confirmed by the evident closeness between the analytical predictions and the actual simulation results considering, for the sake of argument, four concurrent traffic flows with heterogeneous quality-of-service constraints. That closeness exhibits the ability of the proposed analysis in fitting the actual behavior of the system, representing a suitable theoretical tool to support resource allocation strategies, without violating service constraints.

Automated summary

This paper models the probabilistic end-to-end delay bound for virtual reality services in the presence of heterogeneous traffic flows using stochastic network calculus principles, showing network performance analysis under different scheduling policies and multiple traffic flows, with confirmed validity of theoretical bounds when considering concurrent traffic flows with heterogeneous quality-of-service constraints.