Edge Intelligence Empowered Dynamic Offloading and Resource Management of MEC for Smart City Internet of Things  
[PERSON]  
1 China Mobile System Integration Co., Ltd., Xi'an 710077, China; [EMAIL_ADDRESS] (K.T. ); [EMAIL_ADDRESS] (H.C.); [EMAIL_ADDRESS] (Y.L.) [PERSON]  
1 China Mobile System Integration Co., Ltd., Xi'an 710077, China; [EMAIL_ADDRESS] (K.T. ); [EMAIL_ADDRESS] (H.C.); [EMAIL_ADDRESS] (Y.L.) [PERSON]  
1 China Mobile System Integration Co., Ltd., Xi'an 710077, China; [EMAIL_ADDRESS] (K.T. ); [EMAIL_ADDRESS] (H.C.); [EMAIL_ADDRESS] (Y.L.) [PERSON]  
The weighted sum computation rate of all the wireless devices maximization problem was studied. The computing mode and the system resource allocation were jointly optimized by a proposed alternating direction method of multipliers decomposition technique-based algorithm. In [7], a MEC system with a multiple antenna access point (AP) and \(K\) single antenna users was studied. The beamforming vector of the AP, the CPU frequencies, the numbers of offloaded bits and the time allocation of the users were jointly optimized to minimize the energy consumption of the AP. A device-to-device (D2D)-MEC system including one MEC server and multiple user devices was considered in [8]. The goal of this paper was to maximize the number of devices serviced by the system under the communication and computation resources constraints. Unlike the cloud computing, the MEC server has limited computing power. Hence, in single base station (BS) or MEC server scenarios, the IoT devices may have the problem of long service response time and even failure during periods of peak demand. The authors in [9] studied a heterogeneous network consisting of a multi-antenna macro-cell BS and multiple small-cells BSs. The offloading decision, offloading and computation resources allocation were optimized to minimize the total energy consumption of the devices within the coverage of the BSs. In [10], a dense small-cell network was concerned, which had multiple MEC servers. The spatial demand coupling, service heterogeneity, and decentralized coordination problems were solved by a proposed collaborative service placement algorithm. In [11], the weighted sum of the difference of the observed delay and its corresponding delay requirement at each slice was minimized through optimizing the offloading decisions of the users and the communication and computing resource allocation in a multi-cell MEC server network. The above works focused on the MEC problems in static environment, which is a particular case of dynamic environment. In dynamic environment, the MEC system state changes randomly and unpredictable, which is more approximate to the practical scenarios. In static environment, the MEC systems mainly concern about the short-term utility, while in dynamic environment, the long-term utility are concerned. Edge intelligence empowered by artificial intelligence (AI) is promising way to optimize the system performance in the field of the smart city IoT [12; 13; 14]. In [15], the power control and computing resource allocation optimization problem in Industrial Internet of Things MEC network was studied, a deep reinforcement learning (DRL)-based dynamic resource management algorithm was proposed to minimize the long-term average delay of the tasks. In [16], a content caching problem was investigated, and an actor-critic DRL-based algorithm was studied to maximize the cache bit rate. In [17], the task migration problem was studied in multi-MEC server and multi-user network, a multi-agent DRL task migration algorithm was proposed to solve the formulated problem. In [18], a multi-user end-edge-cloud orchestrated network was proposed and a DRL-based computation offloading and resource allocation strategy was designed to minimize the energy consumption of the system. In practical scenarios, there are many metrics to measure the performance of a MEC system, hence, the system requirements are always multifaceted.