When to preempt? Age of information minimization under link capacity constraint

Abstract

In this paper, we consider a scenario where a source continuously monitors an object and sends time-stamped status updates to a destination through a rate-limited link. We assume updates arrive randomly at the source according to a Bernoulli process. Due to the link capacity constraint, it takes multiple time slots for the source to complete the transmission of an update. Therefore, when a new update arrives at the source during the transmission of another update, the source needs to decide whether to skip the new arrival or to switch to it, in order to minimize the expected average age of information (AoI) at the destination. We start with the setting where all updates are of the same size, and prove that within a broadly defined class of online policies, the optimal policy should be a renewal policy, and has a sequential switching property. We then show that the optimal decision of the source in any time slot has threshold structures, and only depends on the age of the update being transmitted and the AoI at the destination. We then consider the setting where updates are of different sizes, and show that the optimal Markovian policy also has a multiple-threshold structure. For each of the settings, we explicitly identify the thresholds by formulating the problem as a Markov Decision Process (MDP), and solve it through value iteration. Special structural properties of the corresponding optimal policy are utilized to reduce the computational complexity of the value iteration algorithm.Index Terms-Age of information, preemptive policies, threshold structure, MDP, structured value iteration.

Timely Cloud Computing: Preemption and Waiting

“…Lots of work pertaining to AoI minimization have appeared in the recent literature, with frameworks that include queuing, optimization and scheduling, energy harvesting, remote estimation, and coding, see, e.g., [1]- [14]. Of particular relevance to our work are those in [15]- [23], which show that the notion of preemption of updates in service and replacing them by new ones is viable for AoI minimization in various settings, which is mainly owing to the nature of AoI that promotes sending fresh updates. This is discussed in a queuing framework in [15]- [17], and more recently in [18] that also extends to the case of multiple sources.…”

“…The first method, and quite the simplest one, is by completing the square in the Lagrangian in (23). Specifically, (23) can be rewritten equivalently as L =…”

Age of Information Analysis for Dynamic Spectrum Sharing

“…Fortunately, the relation between the optimal value of the constraint CMDP in (5) and the optimal value of the relaxed unconstrained MDP in (7) can be expressed as follows [13].…”

Minimum Age of Information in the Internet of Things With Non-Uniform Status Packet Sizes

“…Prior results [8]- [18] are no longer applicable for such a scenario, as they assume uniform status update packet sizes and a network in which one status update can be delivered in one transmission slot. Recently, the authors in [24] proposed optimal status update policies to minimize the average AoI for a status monitoring system with uniform and non-uniform packet sizes. However, the focus of [24] is restricted to a system with a single source and random arrivals of status updates.…”

“…Recently, the authors in [24] proposed optimal status update policies to minimize the average AoI for a status monitoring system with uniform and non-uniform packet sizes. However, the focus of [24] is restricted to a system with a single source and random arrivals of status updates. Indeed, scenarios in which there exists multiple sources whose status updates can be generated at will by the devices, are not considered in [24].…”