Abstract
In this paper, we work on a Cache and Multi-layer MEC enabled C-RAN (CMM-CRAN) to handle various user tasks with minimized latency and energy cost. We intend to solve two particular problems of CMM-CRAN. First, because CMM-CRAN has to maximally cache the most frequently requested data from Service Provide Server (SPS) to Remote Radio Head (RRH) and later offered to proximity mobile users, the cache content placement from SPSs to RRHs becomes a many-to-many matching problem with peer effects. Second, because of multi-layer MEC, a user task has to be dynamically controlled to be offloaded to the best fit cloud, i.e., either local MEC or remote MEC, to get served. This dynamic task offloading is a Multi-Dimension Multiple-Choice Knapsack (MMCK) problem. To solve these two problems, we provide a Joint Cache content placement and task Offloading Solution (JCOS) to CMM-CRAN that utilizes Proportional Fairness (PF) as the user scheduling policy. JCOS applies a Gale-Shaply (GS) method to work out the cache content placement, and a Population Evolution (PE) game theory coupled with a use of Analytic Hierarchy Process(AHP) to work out the dynamic user task offloading. According to the simulation results, CMM-CRAN with JCOS is proved to be able to provide highly desired low-latency communication and computation services with decreased energy cost to mobile users.
Highlights
Nowadays, large-scale field trials on C-RAN has been carried out in various provinces and cities across China
This paper provides a Joint Cache content placement and user task Offloading Solution (JCOS) to a cache and multi-layer MEC enabled C-RAN
The CCPA in JCOS is to make sure the User Equipment (UE) most interested social-aware contents being cached into the storage-constrained Remote Radio Head (RRH) to further save fronthaul data transmission
Summary
Large-scale field trials on C-RAN has been carried out in various provinces and cities across China. To solve this problem, MEC has been proposed to be incorporated into C-RAN to provide computing task offloading options to mobile users to extend their computation ability. C-RAN with MEC deployed in high level is still less able to provide highly desired low-latency computing and communication services to mobile users in low energy cost This is because the fronthaul constraint is a major issue of C-RAN. Because of multi-layer MEC, a user task in CMM-CRAN has to be controlled to be dynamically offloaded to the bet fit cloud, i.e., either to LEC or HEC. With JCOS, UE tasks in CMM-CRAN are easier to obtain the frequently requested content through cache, and the computation tasks can be handled by the best fit edge cloud guaranteeing the benefits of both mobile users and the network.
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