Resource Allocation In Mobile Edge Computing System Based On Data Compression

Nowadays,more and more mobile applications require high computing capacity for providing smart services.However,mobile devices usually have limited computing ability and battery capacity,and it is difficult to support these applications with intensive computation.To tackle the problem,mobile-edge computing(MEC)is proposed as a promising solution by providing computing service to mobile users at the network edge.Data compression technology can reduce the size of the data,thereby reducing the delay and energy consumption during transmission.This dissertation integrates the theory of data compression into the MEC system,and studys the resource allocation in the MEC system.Besides,we further studies the resource allocation problem in the cloud computing and edge computing collaborative system,and through computer simulations,the proposed algorithm is verified and analyzed.The main tasks of this dissertation are as follows:(1)A problem of minimizing the total energy consumption of the system subject to the constraints of communication and computing resources is proposed.First,an energy consumption model related to data compression ratio is established.Then,based on this compression model,an optimization problem with the minimum total energy consumption of the system as the optimization goal is established.At the same time,the compression and transmission tasks need to be completed within the maximum latency,and the MEC server has limited computing resources.To solve this problem,the optimization problem is decomposed into two sub-problems.The first one is related to the allocation of communication bandwidth resources.The optimal solution of the bandwidth allocation can be solved by the Lagrangian method using the KKT condition.Another sub-problem corresponds to the distribution of data compression ratio,and the solution of this sub-problem can be solved by the DC programming method.Therefore,the original optimization problem can be solved by the BCD algorithm.By comparing with two benchmark schemes,the simulation results show that the proposed scheme has excellent performance in reducing the total energy consumption of the system.(2)On the basis of the second chapter,considering the mobile terminal equipment with variable transmission power,the bandwidth optimization in the communication process is extended to the resource allocation optimization in the communication process,so as to realize the minimal total system energy consumption under the maximum tolerance and transmission power limitations.The joint optimization problem of resource allocation and data compression based on mobile edge computing system is studied.The system model,wireless communication model,time delay and energy consumption model and optimization problem model are given in turn.The optimization problem is decomposed into two sub-problems and the BCD algorithm is used to solve the optimization problem in an iterative method.For the first sub-problem,we use the multi-ratio FP algorithm to solve it iteratively.For the second sub-problem,we propose a CCCP algorithm based on DC planning,transform it into a convex optimization problem.Finally,the proposed scheme is simulated and analyzed,and compared with the two benchmark schemes.(3)In the cloud and edge cloud coexistence system,the collaboration between cloud computing and edge computing is studied,that is,the tasks of mobile terminal devices can be processed on edge cloud nodes and cloud servers.First,a compression model in the cloud and edge cloud coexistence system is established,and communication and computing resource allocation are combined to minimize time delay.In particular,we studied the hierarchical compression system in a multi-cell mobile cellular network,where each base station has limited edge computing capabilities.In such a system,the compression task of the mobile device can be processed at the corresponding edge server located in the base station,or transmitted to the cloud server deployed at the cloud center for processing.We solved the following two basic problems: 1)Collaborating edge nodes and cloud servers to achieve optimal computing performance;2)Co-allocating communication and computing resources to minimize the endto-end latency of mobile devices.Compared with the two benchmark schemes,the simulation results show that the scheme proposed in this paper has excellent performance in reducing the total system delay.