D2D-Assisted Computation Offloading Strategy In MEC Systems

The rapid development and increasing popularity of wireless communication technology and mobile Internet have led to the emergence of many new computing-intensive applications,such as Augmented Reality(AR),Virtual Reality(VR),and so on,which require high computing capacity and huge energy consumption.Due to the limited computing resources and battery capacity,smart terminals cannot meet the application's demand for computing capability.However,the existing Mobile Cloud Computing(MCC)architecture is also difficult to meet the user's demands of low latency and high quality because of the remote transmission distance.Mobile Edge Computing(MEC)technology provides IT services and cloud computing capabilities at the edge of the Radio Access Network(RAN)by deploying MEC servers on the user side.The MEC architecture can overcome the shortcomings of the MCC by offloading the application to the MEC server,which can also achieve efficient and low-latency task execution and solve the problem of limited computing capability and battery capacity of the smart terminal.This paper is devoted to researching the computation offloading strategies in MEC systems exploiting Device-to-Device(D2D)communication.The main work is as follows1)The optimization of task offloading energy consumption under the delay constraint in the mobile edge computing system with D2D collaboration is investigated An optimal task assignment strategy is proposed to achieve efficient task offloading in this work.The terminals can choose to execute their own tasks locally or offload the tasks to idle users who can establish a D2D connection with them.A delay penalty mechanism is established to ensure that the tasks are completed within the delay constraint.Based on the penalty mechanism,the problem of minimizing the utilities of all tasks in the system is constructed.The utility function of each task consists of two parts,one is the energy consumption of task execution,and the other is the delay penalty utility.The problem is modeled as a minimum weight matching problem,and a graph matching method is utilized to solve the optimal task assignment problem.A low-complexity matching algorithm is also proposed in this work.Finally,the effectiveness of the proposed collaborative computing offloading scheme is verified by simulation results.The simulation results show that the proposed scheme can effectively guarantee task execution delay and reduce the energy consumption,and the proposed algorithm can achieve approximately optimal performance with low complexity.2)The incentive mechanism of D2D collaborative computing offloading in mobile edge computing system is investigated.A reasonable incentive mechanism considering price strategy and computing resource assignment strategy is proposed to encourage idle users to open their own computing resources owing to the fact that the idle users will not open their own computing resources for free.First,the utility functions of task user and idle user are defined.To optimize the users'utilities,the price process is modeled as a Stackelberg game and a ternary search algorithm is proposed to reach the price-resource Stackelberg equilibrium.After that,to maximize the profits of terminals with tasks,the task assignment problem is formulated and an optimal task assignment method based on the maximum weight matching algorithm is proposed.Finally,simulations are performed to verify the effectiveness of the proposed cooperative computing offloading incentive mechanism.The simulation results show that the proposed scheme can achieve higher computing profits and encourage more terminals to open their own computing resources.