Statistical Age-of-Information Bounds for Parallel Systems: When Do Independent Channels Make a Difference?

Abstract

This paper contributes tail bounds of the age-ofinformation of a general class of parallel systems and explores their potential. Parallel systems arise in relevant cases, such as in multi-band mobile networks, multi-technology wireless access, or multi-path protocols, just to name a few. Typically, control over each communication channel is limited and random service outages and congestion cause buffering that impairs the age-ofinformation. The parallel use of independent channels promises a remedy, since outages on one channel may be compensated for by another. Surprisingly, for the well-known case of M|M|1 queues we find the opposite: pooling capacity in one channel performs better than a parallel system with the same total capacity. A generalization is not possible since there are no solutions for other types of parallel queues at hand. In this work, we prove a dual representation of age-of-information in min-plus algebra that connects to queueing models known from the theory of effective bandwidth/capacity and the stochastic network calculus. Exploiting these methods, we derive tail bounds of the age-of-information of G|G|1 queues. Tail bounds of the age-of-information of independent parallel queues follow readily. In addition to parallel classical queues, we investigate Markov channels where, depending on the memory of the channel, we show the true advantage of parallel systems. We continue to investigate this new finding and provide insight into when capacity should be pooled in one channel or when independent parallel channels perform better. We complement our analysis with simulation results and evaluate different update policies, scheduling policies, and the use of heterogeneous channels that is most relevant for latest multi-band networks.

Priority-Aware Actuation Update Scheme in Heterogeneous Industrial Networks

“…To address the AoI control problem considering heterogeneous networks, there have been several works [ 18 , 19 , 20 , 21 , 22 , 23 ]. These can be categorized into the following: (1) status update design [ 19 , 20 , 21 , 22 ]; and (2) actuation update design [ 18 , 23 ].…”

“…To address the AoI control problem considering heterogeneous networks, there have been several works [ 18 , 19 , 20 , 21 , 22 , 23 ]. These can be categorized into the following: (1) status update design [ 19 , 20 , 21 , 22 ]; and (2) actuation update design [ 18 , 23 ]. Pan et al [ 19 ] determined the scheduling policy to transmit status updates over an unreliable but fast channel or a slow, reliable channel to minimize AoI.…”

“…For the system model, M/M/1 queuing models with different service rates among the servers were assumed. Fidler et al [ 21 ] showed the effect of independent parallel channels on AoI based on the queuing models. Specifically, G/G/1 queuing models with Markov channels were used for the parallel systems with a time-varying capacity.…”