# Resource Allocation on Blockchain Enabled Mobile Edge Computing System
## 1 Introduction  
With the development of intelligent science and technology, more and more mobile devices have access to the mobile network wirelessly. According to Cisco's report in 2019 [1]: there will be 29.3 billion mobile devices by 2023, up from 18.4 billion in 2018. The burst of mobile devices would lead to a rapid increase in data service. Faced with the burden of growing network traffic and the requirements of high-quality service, the traditional wireless communication network is under great pressure. The European Telecommunications Standards Institute (ETSI) proposed the concept of Mobile Edge Computing (MEC) [2] to cater the demands of massive connections and low latency services, which provides computational processing and data storage capabilities for these mobile devices at the edge of the network [3, 4]. MEC mitigates the end-to-end latency of service delivery by moving the cloud computing platform to the edge of the network. Furthermore, it can hold various business scenarios such as smart device applications, health monitoring, connected vehicles, 5G network data migration service [5, 6, 7], and even a satellite-terrestrial network [8]. Therefore, the MEC has attracted extensive attention from the industry since its birth. This new mode can not only bring changes to traditional service providers, but also affect theinteraction modes of many industries and networks. However, considering that MEC is a distributed architecture, it will be very different from cloud computing in processing uploading tasks. Hence, it is necessary to comprehensively consider the delay of various tasks and whether the processing mode of tasks are economical enough. The design of a reasonable resource allocation policy in MEC system has become the focus of research in the industry. At present, the research on resource allocation of MEC has made great progress. Allocation policies can be categorized into many different types based on different classification criteria. The policies can be classified into static and dynamic ones according to whether they are adjusted according to the current system status. According to whether the task is completely allocated to MEC system for processing, it can be divided into full allocation and partial allocation. The complexity of different allocation policies are also different. For example, partial allocation needs to consider processing the task locally, and the local processing time needs to be compared with the processing time of uploading to the MEC server (the delay of wireless transmission needs to be taken into account) when making decisions. This allocation policy is more complicated and needs more consideration and [9]. Researchers have proposed many solutions to the resource allocation problem. Some algorithms are mentioned in the survey of optimization algorithms conducted by [PERSON] et al. [10], such as numerical methods including the Bisection method, Newton-Raphson, and bio-inspired algorithms, which are often widely used to solve resource allocation problems. [PERSON] et al. [11] applied Lyapunov Optimization for resource allocation, which is also a traditional one. Some work applies reinforcement learning, a state-of-the-art methodology. A deep Q-learning method [12] is used to optimize the real-time adaptive policy of computing resource allocation for multi-user unloading tasks. To solve the problem of joint subchannel allocation and power allocation in uplink, [PERSON] et al. [13] proposed three frameworks based on discrete DRL, continuous DRL and joint DRL to solve the above non-convex optimization problem. Furthermore, [PERSON] et al. [14] proposed NLP-based multiagent algorithm for a distributed information system, which allows the built of dynamic and organized overlay network. These policies can balance the delay of task processing and the cost of server well, and thus provide theoretical support for the application of MEC system. Moreover, security is a problem that cannot be ignored in the MEC system [15].