Student Admission Matching based Content-Cache Allocation

As a support to the backend storage, the content caching technique is of great importance to online social networks (e.g., Facebook), in reducing the request service latency and improving user satisfaction. However, the limited caching capacity and booming user data pose great challenges for the content-cache allocation. In this paper, we propose a three-layer content caching model, and focus on how to efficiently allocation contents to caches in order to minimize the overall service latency. We try to tackle this issue by utilizing both centralized Mix Integer Linear Programming (MILP) optimization and by modeling it as a distributed student admission (SA) stable matching problem. In the SA model, we leverage the resident-oriented Gale-Shapley (RGS) algorithm to yield a stable matching between contents and cache centers. We compare the performance between the centralized and distributed algorithms in terms of system welfare and computation analysis. Through numerical results, we prove the effectiveness of our proposed methods.