Researchers all over the world are employing a variety of analysis approaches in attempt to provide a safer and faster solution for sharing resources via a Multi-access Edge Computing system. Multi-access Edge Computing (MEC) is a job-sharing method within the edge server network whose main aim is to maximize the pace of the computing process, resulting in a more powerful and enhanced user experience. Although there are many other options when it comes to determining the fastest method for computing processes, our paper introduces a rather more extensive change to the system model to assure no data loss and/or task failure due to any scrutiny in the edge node cluster. RAFT, a powerful consensus algorithm, can be used to introduce an auction theory approach in our system, which enables the edge device to make the best decision possible regarding how to respond to a request from the client. Through the use of the RAFT consensus, blockchain may be used to improve the safety, security, and efficiency of applications by deploying it on trustful edge base stations. In addition to discussing the best-distributed system approach for our (MEC) system, a Deep Deterministic Policy Gradient (DDPG) algorithm is also presented in order to reduce overall system latency. Assumed in our proposal is the existence of a cluster of N Edge nodes, each containing a series of tasks that require execution. A DDPG algorithm is implemented in this cluster so that an auction can be held within the cluster of edge nodes to decide which edge node is best suited for performing the task provided by the client.

翻译：全球研究人员正采用多种分析方法，试图为通过多接入边缘计算系统共享资源提供更安全、更快速的解决方案。多接入边缘计算是一种边缘服务器网络内的任务共享方法，其主要目标是最大化计算过程的速度，从而提供更强大和增强的用户体验。尽管在确定计算过程的最快方法方面存在许多其他选择，但本文对系统模型进行了更为广泛的改进，以确保边缘节点集群中不会因任何故障而导致数据丢失和/或任务失败。RAFT作为一种强大的共识算法，可在我们的系统中引入拍卖理论方法，使边缘设备能够就如何响应客户端请求做出最佳决策。通过使用RAFT共识，区块链可部署在可信边缘基站上，从而提高应用程序的安全性、可靠性和效率。除了讨论适用于我们多接入边缘计算系统的最佳分布式系统方法外，本文还提出了深度确定性策略梯度算法以降低整体系统延迟。我们的方案假设存在一个由N个边缘节点组成的集群，每个节点包含一系列需要执行的任务。在该集群中实施深度确定性策略梯度算法，以便在边缘节点集群内进行拍卖，从而确定哪个边缘节点最适合执行客户端提供的任务。