Research On Resource Management Technology In Mobile Edge Computing Power Network

With the continuous development of computing technology,network technology,and artificial intelligence,a steady stream of novel intelligent applications,such as automatic driving,VR rendering,and the metaverse,have emerged.These applications have the characteristics of big data,computing-intensive,and delay-sensitive,which need additional resources to ensure the normal functioning and quality of experience(QoE)of users.The currently deployed mobile communication networks are unable to meet the pervasive,on-demand,and real-time resource requirements of intelligent applications.To address the aforementioned challenges,the concept of the edge computing power network has been proposed and has garnered considerable attention in advanced research areas.The mobile edge computing power network,characterized by the deep integration of computing and network capabilities,aims to provide computing support for the future intelligent society in the form of computing services.In the mobile edge computing power network,an effective approach to meet the high demand for intelligent application services involves the reasonable allocation and scheduling of diverse network resources.However,considering the complex and dynamic network environment and the actual requirement of intelligent applications,mobile edge computing power networks still face three challenges,such as the difficulty in resource secure management,the difficulty in task dynamic scheduling,and the difficulty in trusted collaboration among nodes.Firstly,the decision-making information in the process of resource management is susceptible to attacks and exploitation by malicious organizations.Traditional blockchain-based resource management schemes exhibit limitations and fail to meet security requirements.How to enhance blockchain performance and design a secure resource management scheme based on high-performance blockchain is an important research topic.Secondly,the network experiences time-varying resource states,transmission conditions,and interest relationships.These dynamic factors give rise to a range of problems,such as inadequate realtime information perception,inefficient resource scheduling,and imbalances between resource supply and demand.How to comprehensively consider these dynamic factors and devise a real-time and efficient task scheduling strategy is an important research topic.Lastly,heterogeneous nodes in the network pose several challenges,including significant resource disparities,difficulties in information sharing,and untrustworthy behavior.These problems present considerable obstacles when designing collaboration strategies among nodes.How to design a trusted resource collaboration scheme among heterogeneous nodes by perceiving the resource information,and improving the resource utilization of the whole network while meeting usersy requirements is an important research topic.Based on the preceding discussion,this paper primarily concentrates on resource management technology in the mobile edge computing power network.By leveraging the synergies of blockchain technology,artificial intelligence,and optimization theory,we present three key contributions:the resource secure management scheme,the task dynamic scheduling scheme,and the trusted collaboration scheme among heterogeneous nodes.These schemes aim to enhance overall resource utilization across the network,address the aforementioned challenges,and cater to the service requirements of intelligent applications.The detailed contributions and main innovations of this paper are as follows.In order to address the resource security management challenge,we present a blockchain-based resource management scheme.Firstly,we develop a spatialIy-structured high-performance blockchain.Leveraging this structure,we propose a collaborative mining strategy that facilitates cooperation between mobile devices and edge nodes,thereby improving block production efficiency.Secondly,we design a reputation-based consensus mechanism that comprehensively evaluates device reputation by considering their heterogeneous capabilities and historical behaviors.This mechanism enables the assignment of different mining tasks to devices based on reputation,thereby enhancing consensus efficiency.Lastly,we formulate an optimization problem aimed at maximizing network transaction throughput through joint optimization of bandwidth and computing resources.To solve this problem,we present a two-stage iteration algorithm based on the successive convex approximation technique and the Dinkelbach theory.Simulation results demonstrate the notable advantages of our proposed scheme in terms of network transaction throughput performance.To address the dynamic scheduling challenge of tasks,we present a dynamic scheduling scheme for tasks based on a dynamic incentive strategy.Firstly,we introduce a node resource self-sensing method that utilizes blockchain as the primary information-sharing platform,ensuring real-time and reliable information dissemination.Secondly,we employ the Starkelberg game theory to analyze the competitive relationship between the resource-requesting node and the cooperative node.The resource dynamic pricing problem for the requesting node and the resource selling problem for the cooperative node are formulated with the objective of maximizing node revenue.Lastly,we design a dynamic pricing algorithm using reinforcement learning and develop a resource selling algorithm based on Lagrange duality theory to address the dynamic and self-interest aspects of the task scheduling process.Extensive simulation results verify the effectiveness of our proposed scheme in achieving task dynamic scheduling,thereby ensuring the realtime requirements of users.To address the trusted collaboration problem among nodes in the network,we propose a trust-aware collaboration scheme.Firstly,we introduce a trust evaluation method that combines both direct and indirect trust of nodes.This comprehensive trust value effectively reflects the resource capacity and behavioral performance of nodes.Secondly,we design a collaboration strategy between heterogeneous nodes based on the trust value.This strategy encompasses collaborator selection,bandwidth resource allocation,and rendering resource allocation,all modeled as a dynamic optimization problem aimed at ensuring optimal quality of service for users.Finally,we employ a multi-agent reinforcement learning algorithm to solve this dynamic,distributed,and complex problem.Simulation results demonstrate that our proposed scheme facilitates efficient and reliable resource cooperation among heterogeneous nodes,ultimately enhancing the user’s quality of experience(QoE).