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In this paper, we consider a scenario where a source continuously monitors anobject and sends time-stamped status updates to a destination through arate-limited link. We assume updates arrive randomly at the source according toa Bernoulli process. Due to the link capacity constraint, it takes multipletime slots for the source to complete the transmission of an update. Therefore,when a new update arrives at the source during the transmission of anotherupdate, the source needs to decide whether to skip the new arrival or to switchto it, in order to minimize the expected average age of information (AoI) atthe destination. We start with the setting where all updates are of the samesize, and prove that within a broadly defined class of online policies, theoptimal policy should be a renewal policy, and has a sequential switchingproperty. We then show that the optimal decision of the source in any time slothas threshold structures, and only depends on the age of the update beingtransmitted and the AoI at the destination. We then consider the setting whereupdates are of different sizes, and show that the optimal Markovian policy alsohas a multiple-threshold structure. For each of the settings, we explicitlyidentify the thresholds by formulating the problem as a Markov Decision Process(MDP), and solve it through value iteration. Special structural properties ofthe corresponding optimal policy are utilized to reduce the computationalcomplexity of the value iteration algorithm.

When to Preempt? Age of Information Minimization under Link Capacity Constraint