Caching with dual costs

Caching mechanisms in distributed and social settings face the issue that the items can frequently change, requiring the cached versions to be updated to maintain coherence. There is thus a trade-off between incurring cache misses on read requests and cache hits on update requests. Motivated by this we consider the following dual cost variant of the classical caching problem: each request for an item can be either a read or a write. If the request is read and the item is not in the cache, then a read-miss cost is incurred and if the request is write and the item is in the cache, then a write-hit cost is incurred. The goal is to design a caching algorithm that minimizes the sum of read-miss and write-hit costs. We study online and offline algorithms for this problem. For the online version of the problem, we obtain an efficient algorithm whose cost is provably close to near-optimal cost. This algorithm builds on online algorithms for classical caching and metrical task systems, using them as black boxes. For the offline version, we obtain an optimal deterministic algorithm that is based on a minimum cost flow. Experiments on real and synthetic data show that our online algorithm incurs much less cost compared to natural baselines, while utilizing cache even better; furthermore, they also show that the online algorithm is close to the offline optimum.