As the computing resources and the battery capacity of mobile devices are usually limited, it is a feasible solution to offload the computation-intensive tasks generated by mobile devices to edge servers in mobile edge computing (MEC). In this paper, we study the multi-user multi-server task offloading problem in mobile edge computing systems, where all the users compete for the limited communication resources and computing resources. We formulate the offloading problem with the goal of minimizing the cost of the users and maximizing the profits of the edge servers. We propose a hierarchical Economic and Efficient Task Offloading and Resource Purchasing (EETORP) framework that includes a two-stage joint optimization process. Then, we prove that the problem is NP-complete. For the first stage, we formulate the offloading problem as a multi-channel access game (MCA-Game) and prove theoretically the existence of at least one Nash equilibrium strategy in the MCA-Game. Next, we propose a game-based multi-channel access (GMCA) algorithm to obtain the Nash equilibrium strategy and analyze the performance guarantee of the obtained offloading strategy in the worst case. For the second stage, we model the computing resource allocation between the users and edge servers by Stackelberg game theory, and reformulate the problem as a resource pricing and purchasing game (PAP-Game). We prove theoretically the property of incentive compatibility and the existence of Stackelberg equilibrium. A game-based pricing and purchasing (GPAP) algorithm is proposed. Finally, a series of both parameter experiments and comparison experiments are carried out, which validate the convergence and effectiveness of the GMCA and GPAP algorithms.
The development of Internet of Things (IoT) technology depends on technologies such as high-efficiency storage and high computing power. Mobile cloud computing (MCC) technology will be an important foundation for the development of IoT. The efficient scheduling of tasks in IoT devices in MCC environment is challenging. The requirements for task scheduling in MCC are becoming more and more complex. As the core problem in MCC, task scheduling aims to allocate tasks reasonably, achieve optimal scheduling strategies, and complete tasks effectively. In this paper, efficient delay-aware task scheduling algorithm (EDTSA) is proposed, with the optimization goal of minimizing task running time. The matching of tasks and virtual machines is modeled as a bipartite graph. The problem is divided into multiple subproblems to solve the optimal solution separately. The combined solution is used as the initial solution of the local search algorithm. The efficiency of the local search depends on the quality and nature of the initial solution. We can generate multiple initial solutions according to different division criteria. The initial solution is the combination of the optimal solutions of the subproblems, so the quality of the initial solution has been greatly improved and generating multiple initial solutions according to the division can reduce the probability of falling into the local optimal solution. This algorithm also divides the neighborhood to reduce unnecessary searches. Finally, we verify the efficiency and practicability of the algorithm through experiments.
Urban Internet of Things (IoT) plays an extremely important role in our daily life by deploying smart cities and urban brains. Orthogonal multiple access (OMA) technology has been a commonly used communication method in recent years, but nonorthogonal multiple access (NOMA) attracts the attention of many researchers due to its superiority of successive interference cancellation (SIC) technology. We consider adding the base station (BS) and unmanned aerial vehicle (UAV) to perform collaborative data offloading services with urban IoT devices and introduce the NOMA technology to improve offloading efficiency. In order to solve the data unloading problem in this model cost-effectively, we formulate the model as a game model based on noncooperative competition and propose the iterative game-based data offloading algorithm (GDOA) to obtain the Nash equilibrium (NE) solution. Finally, we use the simulation data to conduct parametric analysis experiments and comparison experiments on GDOA to evaluate its real performance.
Integrating nonorthogonal multiple access (NOMA) and edge computing into the Internet of Things (IoT) for resource allocation and computing offloading can effectively reduce delay and energy consumption and improve spectrum efficiency. Computation tasks can be split into several independent subtasks and can be locally processed by IoT devices or offloaded to the MEC servers to process. The limited computing resources deteriorate the system performance. Thus, it is crucial to design the reasonable allocation strategies of computation resource and transmission power resource. In this paper, we jointly determine the CPU-cycle frequency allocation and transmission power allocation and formulate a stochastic optimization to minimize the energy consumption of IoT devices. Based on the Lyapunov optimization theory, we decompose the optimization problem into two deterministic subproblems to solve separately. One of them is obtained by seeking the first derivative, and the other is solved by using the best response idea after establishing the game model. Meanwhile, we propose a dynamic resource allocation and task offloading (DRATO) algorithm. Moreover, the simulation experiments show that the proposed algorithm effectively improves system performance and reduces energy consumption compared to three other benchmark methods.
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