关键词： Task analysis   Servers   Resource management   Delays   Optimization   Costs   Interference   Ultra-dense networks   mobile edge computing   system cost   resources scheduling   task offloading  

摘要： With the development of 5G communication technologies and smart mobile devices, various computation-intensive and delay-sensitive tasks continue to increase. The combination of Mobile Edge Computing (MEC) and Ultra-Dense Networks (UDN) increases the network capacity and improves the computing capability of mobile devices, which effectively meets the transmission and computing demands of tasks. However, the ultra-dense deployment of network infrastructures causes energy shortage and channel interference, making it challenging to reduce the system cost. In this paper, we investigate the task offloading and resources scheduling problem in UDN with MEC. In order to minimize the total system cost including delay and energy consumption in the intensive deployment environment of edge servers and base stations (BSs) simultaneously, we design the strategy of task offloading, BS selection and resources scheduling of mobile devices. Because of the complex coupling of decision variables, the original problem is decomposed into two sub-problems. We propose Newton-IPM based Computing Resource Allocation (NICRA) algorithm and Genetic Algorithm based BS Selection and Resources Scheduling (GABSRS) algorithm to solve these two sub-problems, respectively. Then, we prove the number of iterations can be reduced effectively by the GABSRS algorithm while reaching the optimal solution through mathematical analysis. Through experiments analysis, the effectiveness of the GABSRS algorithm is validated.

关键词： Task analysis   Vehicle dynamics   Resource management   Computer architecture   Heuristic algorithms   Delays   Servers   Internet of Vehicles   mobile edge computing   reinforcement learning  

摘要： The rapid development of the Internet of Vehicles (IoV) leads to various on-board applications including delay-sensitive and compute-intensive applications. However, vehicles with limited computing resources cannot meet the requirements of the delay for these applications. The application of edge computing on the IoV can deal with the above problems. However, most existing edge computing offloading work only considers static scenarios. These schemes are difficult to adapt to dynamic processes under continuous time. To solve this problem, in this article, we propose an end-edge-cloud architecture of vehicles for task computation offloading, where considers three task computing methods. For the dynamically changing environment in the IoV, we utilize an Asynchronous Advantage Actor-Critic (A3C) based computation offloading algorithm to solve the problem and seek optimal offloading decisions. While considering efficiency and fairness, our solution enables vehicle users to obtain computing services from edge servers in real-time and conveniently. The reward function in this paper contains a relative efficiency factor and a relative fairness factor, which are indicators to measure the efficiency of task completion as well as the relative fairness between vehicles. These indicators can be adjusted to obtain more targeted offloading decisions in different scenarios. The experimental results show that the scheme proposed in this paper can achieve good convergence performance and adapt to the dynamic offloading scene. Compared with the other scheme, our scheme can achieve better performance in terms of efficiency of task completion and fairness among tasks. The numerical results show that compared to the Deep Q Network-based scheme, the local computing scheme, the edge computing scheme, and the random scheme, our algorithm can save 7.2%, 18.5%, 41.8%, and 33.5% of the mean for average delay, respectively.

关键词： Internet of Vehicles   Mobile edge computing   Non-orthogonal multiple access   Resource allocation   Lyapunov optimization   Caching technique   Trust management scheme  

摘要： This paper considers a vehicle edge computing system that uses nonOrthogonal multiple access technologies for task uploading and data packet downloading to solve the problems of insufficient computing power on the vehicle side, significant task processing delay, high energy consumption, and lack of wireless resources. The study looks into how to develop, compute offloading, and content caching queues under the vehicle edge computer network, with the help of NOMA, to decrease the total energy consumption at the MEC end. Regardless of the fact that there have been a lot of studies on computational offloading and content caching, the energy consumption of both is rarely investigated and dealt with simultaneously. The simulation findings demonstrate that, compared to typical orthogonal multiple access methods, the nonOrthogonal multiple access offload cache approach in the vehicle edge computing system may considerably lower the system energy consumption.

关键词： mobile edge computing   D2D communication   partial offloading   Q learning   deep Q-network  

摘要： Mobile edge computing (MEC) and device-to-device (D2D) communication can alleviate the resource constraints of mobile devices and reduce communication latency. In this paper, we construct a D2D-MEC framework and study the multi-user cooperative partial offloading and computing resource allocation. We maximize the number of devices under the maximum delay constraints of the application and the limited computing resources. In the considered system, each user can offload its tasks to an edge server and a nearby D2D device. We first formulate the optimization problem as an NP-hard problem and then decouple it into two subproblems. The convex optimization method is used to solve the first subproblem, and the second subproblem is defined as a Markov decision process (MDP). A deep reinforcement learning algorithm based on a deep Q network (DQN) is developed to maximize the amount of tasks that the system can compute. Extensive simulation results demonstrate the effectiveness and superiority of the proposed scheme.

关键词： Task analysis   Peer-to-peer computing   Servers   Costs   Device-to-device communication   Admission control   Resource management   Lyapunov optimization   mobile edge computing   partial offloading   computation resource allocation   admission control   D2D communication   multi-RAT  

摘要： Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to run complex applications. This can be realized through efficient computing data offloading and yet enhanced while simultaneously using multiple wireless interfaces for D2D, MEC and cloud offloading. In this work, we propose user-centric real-time computation task offloading and resource allocation strategies aiming at minimizing energy consumption and monetary cost while maximizing the number of completed tasks. We develop dynamic partial offloading solutions using the Lyapunov drift-plus-penalty optimization approach. Moreover, we propose a task admission solution based on support vector machines (SVM) to assess the potential of a task to be completed within its deadline, and accordingly, decide whether to drop from or add it to the user's queue for processing. Results demonstrate high performance gains of the proposed solution that employs SVM-based task admission and Lyapunov-based computation offloading strategies. Significant increase in number of completed tasks, energy savings, and cost reductions are resulted as compared to alternative baseline approaches.

关键词： Internet of Things   Servers   Relays   Resource management   Task analysis   Optimization   Computational modeling   Mobile edge computing   partial offloading   spectrum sharing   relaying   bipartite matching  

摘要： In this paper, we consider Internet of Things (IoT) based mobile edge computing (MEC) system. IoT devices are controlled by access points (APs), which do not have access to any licensed spectrum. This puts limitations on IoT devices' offloading capability and hence effectively reduces the number of computed bits. Under such scenario, we consider spectrum sharing, where IoT networks get access to licensed spectrum by helping a primary network (owns licensed spectrum) in cooperative relaying. We aim to maximize sum of the primary network's communication rate and IoT networks' total number of computed bits by jointly optimizing relay selection, licensed spectrum allocation, local computation, and offloading sequence selection. Moreover, we consider important constraints, e.g., the guaranteed rate for the primary network, IoT devices' available energy, and available licensed spectrum resource blocks (RBs). We observe that the optimization problem is combinatoric in nature, which becomes more complicated when IoT devices' scheduling order comes into the formulation. From our analysis, we devise an optimal and computationally efficient algorithm. In the result section, we compare with relevant benchmark systems and show the efficacy of our proposed model. Moreover, we also show how IoT and primary networks benefit from spectrum sharing.

关键词： Internet of Things   mobile edge computing   edge node deployment   genetic algorithm  

摘要： Mobile edge computing (MEC) has become an effective solution for insufficient computing and communication problems for the Internet of Things (IoT) applications due to its rich computing resources on the edge side. In multi-terminal scenarios, the deployment scheme of edge nodes has an important impact on system performance and has become an essential issue in end-edge-cloud architecture. In this article, we consider specific factors, such as spatial location, power supply, and urgency requirements of terminals, with respect to building an evaluation model to solve the allocation problem. An evaluation model based on reward, energy consumption, and cost factors is proposed. The genetic algorithm is applied to determine the optimal edge node deployment and allocation strategies. Moreover, we compare the proposed method with the k-means and ant colony algorithms. The results show that the obtained strategies achieve good evaluation results under problem constraints. Furthermore, we conduct comparison tests with different attributes to further test the performance of the proposed method.

关键词： Mobile edge computing   Carbon neutralization   Power monitoring and dispatching   Innovation path  

摘要： In the process of gradual development of power grid, the role of service support and technical support of dispatching secondary system is becoming increasingly prominent, and the operation and maintenance work is also increasing with the number and types of dispatching secondary system, which makes the limitations of traditional monitoring methods more prominent. In this paper, a task unloading and resource allocation optimization (TORA) algorithm is proposed, which decomposes the task unloading decision and resource allocation problem into two subproblems and solves two optimization subproblems, respectively. The feasibility of the algorithm is demonstrated, and the calculation method of the theoretical lower bound of the original problem is given. Experimental results show that TORA, RTO and UTO algorithms can get results close to the theoretical lower bound. When the number of unit tasks is too large, the average task completion time of TORA algorithm is more than 20% less than that of RTO algorithm. When the user task uploads a large amount of data, the average completion time of TORA algorithm task is more than 15% less than that of RTO and UTO algorithms.

关键词： Servers   Device-to-device communication   Task analysis   Resource management   Computational modeling   Cloud computing   Optimization   Mobile edge computing   D2D communication   task offloading   resource allocation   potential game  

摘要： Driven by the visions of Internet of Things and 5G communications, mobile edge computing (MEC) as a new paradigm promises dramatic reduction in latency. In recent years, an increasing number of scholars introduce D2D communication into the MEC system to further reduce the latency. In this letter, we propose a D2D-enabled MEC three-layer partial offloading scenario and design a joint optimization algorithm based on game theory (JOAGT). The proposed algorithm includes offloading mode selection, offloading fraction allocation, and resource allocation. Simulation results show that the proposed scenario and algorithm can significantly reduce the total latency, and the algorithm can exhibit good performance.

关键词： Privacy   Games   Resource management   Smart cities   Task analysis   Investment   Computational modeling   Computation offloading   resource allocation   mobile edge computing   smart cities   IoT   Stackelberg game   privacy  

摘要： Internet of Thing-based mobile devices (MDs) make the vision of smart cities become reality. Nevertheless, MDs are subjected to some shortcomings and cannot effectively handle the explosive growth of applications. Fortunately, the performance of MDs can be augmented by offloading latency-critical tasks to edge service providers (ESPs). Nevertheless, there is a competitive relationship among MDs as the resources of ESPs are limited. Moreover, there is a certain risk of privacy leakage during computation offloading. In view of this, we study the computation offloading and resource allocation which is formulated as a Stackelberg game with the aims of maximizing the utilities of MDs and the profits of ESPs under the consideration of energy efficiency by optimizing the strategies of prices, computation offloading and the privacy investment. Additionally, both the cooperation scenario and non-cooperation among ESPs are investigated. Besides, the social effect of MDs on privacy concerns is also considered. Technically, the Stackelberg equilibrium is solved by utilizing the distributed Alternating Direction Method of Multipliers algorithm in a distributed manner. Numerous simulation results have illustrated that the method is effective and also has fast convergence and high scalability.