Age of Information for Updates with Distortion We consider an information update system where an information receiver requests updates from an information provider in order to minimize its age of information. The updates are generated at the transmitter as a result of completing a set of tasks such as collecting data and performing computations. We refer to this as the update generation process. We model the quality (i.e., distortion) of an update as an increasing (resp. decreasing) function of the processing time spent while generating the update at the transmitter. While processing longer at the transmitter results in a better quality (lower distortion) update, it causes the update to age. We determine the ageoptimal policies for the update request times at the receiver and update processing times at the transmitter subject to a minimum required quality (maximum allowed distortion) constraint on the updates.

Rejuvenation and the Age of Information Two decades after the seminal paper on software aging and rejuvenation appeared in 1995, a new concept and metric referred to as the age of information (AoI) has been gaining attention from practitioners and the research community. In this vision paper, our aim is to show the similarities and differences between software aging and information aging. In particular, modeling frameworks that have been applied to software aging, such as the semi Markov approach can be immediately applied in the realm of age of information. Conversely, we indicate that questions pertaining to sampling costs associated with the age of information can be useful to assess the optimal rejuvenation trigger interval for software systems.