Optimal Scheduling of Age-centric Caching: Tractability and Computation

TL;DR

This paper investigates the complexity and develops algorithms for optimal cache update scheduling to minimize age of information, considering content size variability and capacity constraints, with scalable solutions and performance bounds.

Contribution

It establishes the problem's tractability boundary based on content size, provides an integer linear program, and introduces a scalable column generation algorithm with performance bounds.

Findings

The problem is tractable when contents are of equal size.

The proposed algorithm outperforms greedy scheduling in large-scale scenarios.

A bound on the global optimum enables performance assessment.

Abstract

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.