Optimal Scheduling of Age-centric Caching: Tractability and Computation

The notion of age of information (AoI) has become an important performance metric in network and control systems. Information freshness, represented by AoI, naturally arises in the context of caching. We address optimal scheduling of cache updates for a time-slotted system where the contents vary in size. There is limited capacity for the cache and for making content updates. Each content is associated with a utility function that is monotonically decreasing in the AoI. For this combinatorial optimization problem, we present the following contributions. First, we provide theoretical results settling the boundary of problem tractability. In particular, by a reformulation using network flows, we prove the boundary is essentially determined by whether or not the contents are of equal size. Second, we derive an integer linear formulation for the problem, of which the optimal solution can be obtained for small-scale scenarios. Next, via a mathematical reformulation, we derive a scalable optimization algorithm using repeated column generation. In addition, the algorithm computes a bound of global optimum, that can be used to assess the performance of any scheduling solution. Performance evaluation of large-scale scenarios demonstrates the strengths of the algorithm in comparison to a greedy schedule. Finally, we extend the applicability of our work to cyclic scheduling

Age of Information: An Introduction and Survey

We summarize recent contributions in the broad area of age of information (AoI). In particular, we describe the current state of the art in the design and optimization of low-latency cyberphysical systems and applications in which sources send time-stamped status updates to interested recipients. These applications desire status updates at the recipients to be as timely as possible; however, this is typically constrained by limited system resources. We describe AoI timeliness metrics and present general methods of AoI evaluation analysis that are applicable to a wide variety of sources and systems. Starting from elementary single-server queues, we apply these AoI methods to a range of increasingly complex systems, including energy harvesting sensors transmitting over noisy channels, parallel server systems, queueing networks, and various single-hop and multi-hop wireless networks. We also explore how update age is related to MMSE methods of sampling, estimation and control of stochastic processes. The paper concludes with a review of efforts to employ age optimization in cyberphysical applications