###### Abstract  
Mobile terminal applications with high computing complexity and high time delay sensitivity are developing quite fast today, which aggravates the load of mobile cloud computing and storage and further leads to network congestion and service quality decline. Mobile edge computing (MEC) is a way of breaking through the limits of computing and storage resources of mobile cloud and alleviating the load of mobile cloud. Computing time costs and transmission time costs are considered to be the main issues for the mobile cloud when carrying out computing offloading and data caching. Therefore, an efficient resource management strategy, which could minimize the system delay, is proposed in this paper. The new scheme offloads reasonably computing tasks and caches the tasks' data from the mobile cloud to mobile edge computing-enabled base stations. An intelligence algorithm, genetic algorithm, is being used to solve the global optimization problem which would cause transmission delay and computing resources occupation, and to determine the computing offloading and data caching probability. The simulation of the system using MATLAB is conducted in 8 different scenarios with different parameters. The results show that our new scheme improves the system computing speed and optimizes the user experience in all scenarios, compared with the scheme without data caching and the scheme without computing offloading and data caching. mobile edge computing; resource allocation; computing offloading; data caching optimization +
Footnote †: journal: Applied Sciences  
0000-0001-5802-3300-0000-0000-0000-0000-0000-0000-00000-00000-00000-00000-00000-00000-0000-00000-00000-00000-00000-00000-00000-00000-000000-00000-000000-0  
In our research, we define the MEC stations consists of five units: (1) receiving unit- which is used to receive service requests from its covering MTs and the surrounding MEC stations; (2) control unit: which is used to determine whether the received task is further offloaded and whether cache the data required for the corresponding task from C based on the Cooperative Resource Management Algorithm; (3) caching unit: which is used to cache the data required for the corresponding task based on the Cooperative Resource Management Algorithm to reduce the time delay in data access to C; (4) computing unit: which is used to calculate the computing tasks offloading from its covering MTs; (5) sending unit: which is used to send the calculation result to the MT, send the offloading request to further MEC stations and send the data request of the corresponding task to C. It is worth noting that MEC stations have limited computation and storage resources, so that they cannot provide computing and caching services for all the tasks like C. However, the MEC-BS will be still overloaded if there are too many tasks offloaded from the MTs. In existing algorithms, they try to ease the load of MEC stations through refusing, delaying or queuing the offloading requests of MTs. However, these algorithms will lead to poor QoS of the system. Thus, a new model of resource management is urgently needed. In this paper, our research is concerned with a local system under mobile cloud, which includes a mobile edge computing-enabled base station (MEC-BS), the mobile terminals covered by this MEC-BS, and the neighbor mobile edge computing-enabled base stations within a certain distance from MEC-BS. Our goal is to offload the mobile cloud computing and storage pressure through MEC stations. Because of the poor computing capability of every MTs, we ignore MT as a offloading object to guarantee the quality of service. All tasks of MT need to go through the MT's MEC-BS to offload to mobile cloud or any neighbor mobile edge computing-enabled base station. Figure 1 is the overall architecture of our model.