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The notion of timely status updating is investigated in the context of cloudcomputing. Measurements of a time-varying process of interest are acquired by asensor node, and uploaded to a cloud server to undergo some requiredcomputations. These computations consume random amounts of service time thatare independent and identically distributed across different uploads. After thecomputations are done, the results are delivered to a monitor, constituting anupdate. The goal is to keep the monitor continuously fed with fresh updatesover time, which is assessed by an age-of-information (AoI) metric. A scheduleris employed to optimize the measurement acquisition times. Following an update,an idle waiting period may be imposed by the scheduler before acquiring a newmeasurement. The scheduler also has the capability to preempt a measurement inprogress if its service time grows above a certain cutoff time, and upload afresher measurement in its place. Focusing on stationary deterministicpolicies, in which waiting times are deterministic functions of theinstantaneous AoI and the cutoff time is fixed for all uploads, it is shownthat the optimal waiting policy that minimizes the long term average AoI has athreshold structure, in which a new measurement is uploaded following an updateonly if the AoI grows above a certain threshold that is a function of theservice time distribution and the cutoff time. The optimal cutoff is then foundfor standard and shifted exponential service times. While it has beenpreviously reported that waiting before updating can be beneficial for AoI, itis shown in this work that preemption of late updates can be even morebeneficial.

Timely Cloud Computing: Preemption and Waiting