Optimal Scheduling for Timely Information in Communication Systems

The explosive growth of data in information society poses significant challenges in the timely delivery of information in the context of communication networks. Hence, optimal utilization of scarce network resources is crucial. This dissertation contributes to several aspects related to the timely delivery of information, including scheduling of data flows between sources and destinations in a network, scheduling of content caching in a base station of mobile networks, and scheduling of information collection. Two important metrics, namely, delivery deadline and information freshness, are accounted for. Mathematical models and tailored solution approaches are developed via tools from optimization. Five research papers are included in the dissertation. Paper I studies a flow routing and scheduling problem with delivery deadline. This type of problem arises in many applications such as data exchange in scientific projects or data replication in data centers where large amounts of data need to be timely distributed across the globe. Papers II, III, and IV inves­tigate content caching along time in a base station. Content caching at the network’s edge has recently been considered a cost­efficient way of providing users with their requested informa­tion. In Paper II, the schedule for updating the cache is optimized with respect to the content requests of users and the popularity of contents over time. Paper III, as an extension of Paper II, addresses the question of how to keep the cache information fresh, as all contents can not be up­dated due to the limited capacity of the backhaul link. The freshness of information is quantified via the notion of age of information (AoI). Paper IV investigates joint optimization of content caching as well as recommendation; the latter contributes to satisfying content requests in case of a cache miss. Paper V studies optimal scheduling of information collection from a set of sensor nodes via an unmanned aerial vehicle. The objective is to keep the overall AoI as small as possible. In these studies, analysis of problem complexity is provided, and time­efficient solution al­gorithms based on column generation, Lagrangian decomposition, and graph labeling are de­veloped. The algorithms also yield a bound of global optimum, that can be used to assess the performance of any given solution. The effectiveness of the algorithms in obtaining near­optimal solutions is demonstrated via extensive simulations