# Game-Theory-Based Task Offloading and Resource Scheduling in Cloud-Edge Collaborative Systems
## 1 Introduction  
With the development of IoT and 5G technology, the availability of mobile devices is not limited to wired connections, and a great number of new mobile applications with immersive experiences step into the market, such as virtual reality, connected cars, location awareness, smart cities, etc. Most of these new applications are deployed on mobile devices and are sensitive to real-time communication and intensive computation. Considering the limited resources of mobile devices, it is not feasible to store and process large amounts of multimodal sensory data on these devices. Traditional cloud computing networks [1] need to upload all computing tasks to the cloud center for processing, and it is difficult to avoid problems such as upload delay, network bandwidth, energy loss and data security during transmission. To account for the lack of cloud computing processing, edge computing technology has been proposed and has received a lot of attention. Edge computing [2] is a new computing model that performs computations at the edge of the network close to the user's end device, where the edge is any computing and network resource between the data-generating end and the cloud-centric path. Mobile edge computing (MEC) is the main branch of edge computing. At present, there is no uniform definition of mobile edge computing in academia. The concept given by the European Telecommunications Standardization Institute (ETSI) is that, by deploying edge servers close to mobile users at the network edge, they can use the wireless access network to provide the required services and computing functions nearby. The MEC paradigm provides low-latency, mobility and location-aware support for latency-sensitive applications. In practice, the processing power and computational resources of edge nodes are limited, and the computational resources allocated to mobile users by edge nodes dependon the load level of edge nodes (i.e., the number of parallel tasks offloaded to edge nodes). When a great number of mobile devices offload their task demands to the same edge node, it leads to a higher load on the edge node and increased offload task processing latency. A reasonable offloading strategy can improve the edge server's resource utilization and reduce the offloading task processing latency [3] and system energy consumption [4]. Therefore, the offloading strategy of mobile users and the resource scheduling of edge servers are crucial technologies for edge computing. For instance, during the epidemic prevention and control period, with residents' itinerary information and body temperature data monitoring, a reasonable task offloading strategy can allocate data to the best processing location; therefore, the epidemic prevention department can obtain and process data in a timely manner, reducing data processing delays. According to the optimization objectives of offloading decisions, they can be classified into three categories [5]: reducing delay, reducing energy consumption and balancing delay and energy consumption. [PERSON] et al. [6] researched systems that allow parallel computing to be performed on mobile devices and MEC servers. The average delay and energy consumption of task offloading were analyzed using Markov chain theory. The delay optimization problem was mathematically modeled, and a one-dimensional search algorithm was proposed to obtain the optimal offloading strategy. [PERSON] et al. [7] studied the applications of artificial intelligence techniques in task offloading, proposed an autonomous management framework based on Q-learning techniques and solved the problem by Markov decision process modeling and deep reinforcement learning algorithms. Simulation results show that the proposed autonomous deep learning approach can significantly reduce energy consumption. To minimize the delay while saving energy consumption, [PERSON] et al.