🤖 AI Summary
This study addresses the minimization of the age of information (AoI) and peak AoI (PAoI) in a single-source, single-server continuous-time status update system by optimizing preemption policies. The work proposes the first general analytical framework capable of exactly computing the average AoI and PAoI under arbitrary age-dependent preemption strategies. Leveraging this framework, two practical policies—probabilistic preemption (PP) and threshold-based preemption (TP)—are efficiently optimized via one-dimensional line search. Numerical results under log-normal service times demonstrate that the optimized PP and TP policies significantly reduce both AoI and PAoI, thereby validating the effectiveness and superiority of the proposed approach.
📝 Abstract
In this work, we study a single-source single-server continuous-time status update system where the updates arrive according to a Poisson process and update service times are generally distributed. In our proposed setting, a preemption policy refers to one where a new update preempts the ongoing one with a probability depending on the age of the update in service. We first propose an analytical method to derive the average age of information (AoI) and average peak AoI (PAoI) for any such preemption policy. This analysis is then utilized to tune two particular preemption policies: (i) probabilistic preemption (PP), in which preemption takes place according to a fixed probability regardless of the update age, (ii) threshold-based preemption (TP), for which preemption is incurred when the update age exceeds a certain threshold, both using one-dimensional line search. The effectiveness of policy tuning for the PP and TP policies is validated using lognormal-distributed update service times.