Delay-Optimal Edge Caching With Imperfect Content Fetching via Stochastic Learning

Abstract

Caching popular contents in close proximity to the users can effectively reduce the latency in communication systems. Considering the imperfect content fetching from the original server, the caching management at the edge node should be determined according to not only the content popularity but also how difficult to fetch these content objects from their corresponding original servers. In this paper, we study the edge caching for minimizing the long-term average delay, where the fetching delay of the uncached contents is introduced. We construct a decision-theoretic framework for this delay-optimal content-caching problem, where the main obstacle is that the consideration of the imperfect content fetching breaks the Markovian property in the decision-theoretic framework. To overcome this obstacle, by analyzing the queue dynamics within the content fetching delay after the content object is removed from the cache, we transform the problem for meeting the Markovian property, and model it as an infinite horizon semi-Markov decision process (SMDP). Achieving the delay optimality of the caching problem needs to solve the Bellman equation of the SMDP, but it leads to the curse of dimensionality. We decompose the global optimality equation into several per-content optimality equations, and propose a low-complexity delay-optimal content caching algorithm by stochastic learning for each content. Finally, the simulation results show that our proposed algorithm achieves significantly lower delay than conventional caching algorithms.