Stable Service Caching in MECs of Hierarchical Service Markets With Uncertain Request Rates

Abstract

A 5G hierarchical service market is emerging with both large-scale and small-scale network service providers competing the computing and network bandwidth resources of an infrastructure provider. In this paper, we investigate the problem of caching services originally deployed in remote clouds to cloudlets in an MEC network of a hierarchical service market. We first propose a novel approximation-restricted framework that guarantees the stability of the 5G service market. Under the proposed framework, we first propose an approximation algorithm with an approximation ratio for the problem with non-selfish network service providers. We then design an efficient Stackelberg congestion game with selfish network service providers, and analyze the Price of Anarchy (PoA) of the proposed game to measure its efficiency loss due to selfishness of players. When the request rates of services are not given in advance, we study the service caching problem with request rate uncertainty, and propose an approximation algorithm and a Stackelberg game via leveraging the randomized rounding technique. We finally evaluate the performance of the proposed algorithms and mechanisms by both simulations and implementations in a real test-bed. Results show that the performance of our proposed mechanisms achieve around 9.2% less cost than those of existing approaches.