关键词： Vehicular networks   mobile edge computing   task collaborative offloading   BES algorithm   computation offloading  

摘要： To reduce computing delay and energy consumption in the Vehicular networks, the total cost of task offloading, namely delay and energy consumption, is studied. A task offloading model combining local vehicle computing, MEC (Mobile Edge Computing) server computing, and cloud computing is proposed. The model not only considers the priority relationship of tasks, but also considers the delay and energy consumption of the system. A computational offloading decision method IBES based on an improved bald eagle search optimization algorithm is designed, which introduces Tent chaotic mapping, Levy Flight mechanism and Adaptive weights into the bald eagle search optimization algorithm to increase initial population diversity, enhance local search and global convergence. The simulation results show that the total cost of IBES is 33.07% and 22.73% lower than that of PSO and BES, respectively.

关键词： Task analysis   Resource management   Trajectory   Wireless communication   Servers   Optimization   Manganese   Mobile edge computing   UAV networks   task offloading   cooperative offloading   deep reinforcement learning  

摘要： Mobile edge computing can effectively reduce service latency and improve service quality by offloading computation-intensive tasks to the edges of wireless networks. Due to the characteristic of flexible deployment, wide coverage and reliable wireless communication, unmanned aerial vehicles (UAVs) have been employed as assisted edge clouds (ECs) for large-scale sparely-distributed user equipment. Considering the limited computation and energy capacities of UAVs, a collaborative mobile edge computing system with multiple UAVs and multiple ECs is investigated in this paper. The task offloading issue is addressed to minimize the sum of execution delays and energy consumptions by jointly designing the trajectories, computation task allocation, and communication resource management of UAVs. Moreover, to solve the above non-convex optimization problem, a Markov decision process is formulated for the multi-UAV assisted mobile edge computing system. To obtain the joint strategy of trajectory design, task allocation, and power management, a cooperative multi-agent deep reinforcement learning framework is investigated. Considering the high-dimensional continuous action space, the twin delayed deep deterministic policy gradient algorithm is exploited. The evaluation results demonstrate that our multi-UAV multi-EC task offloading method can achieve better performance compared with the other optimization approaches.

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： Mobile edge computing   Microservices   Service composition   Multi-objective optimization   QoS  

摘要： With the development of software technology, some complex mobile and Internet-of-Things (IoT) applications can be constituted by a set of microservices. At present, mobile edge computing (MEC) has been used for microservice provision to achieve faster response speed and less network pressure. Based on container technology, microservices can be easily deployed in the MEC environment, while multiple microservice instances in multiple locations need to be selected to provide services for a large number of users geographically distributed. How to fully consider the service response time, scheduling the startup and running strategies of microservice instances with the least resource cost for multiple mobile edge servers is the core problem of microservice composition. In this paper, we propose a multi-objective evolutionary approach (MSCMOE) based on improved NSGA-III to minimize the service access delay and network resource consumption in the process of microservice composition. In order to maintain the diversity of the population, we use the improved reference point strategy to enhance the computational efficiency of seeking elite solutions in the non-dominated layer. Experimental results based on a real data set of Shanghai Telecom demonstrate that MSCMOE can effectively reduce network resource consumption while reducing service request time.

关键词： Optimization   Trajectory   Wireless communication   Energy consumption   Task analysis   Indexes   Buildings   Intelligent reflecting surface   mobile edge computing   unmanned aerial vehiclexg   genetic algorithm   tabu search algorithm   K-Means algorithm  

摘要： The aim of this article is to save the energy consumption and tasks' completion time of multi-unmanned aerial vehicle (UAV) by jointly optimizing the trajectories of UAVs and passive phase shifts of intelligent reflecting surface (IRS)s. By applying this approach, the system will lead to complex optimization problem as it has several optimization subproblems like optimization of deployment of stop points (SPs), the optimization of association among Internet of Things devices (IoTDs), SPs, and UAVs, the passive phase shifts of IRSs, and the trajectories planning of UAVs. A trajectory planning and passive beamforming algorithm with variable population size (TPPBA-VP) is proposed to tackle the complex optimization problem via four phases taking into accounts all the above optimization concerns. Simulation results demonstrate that the proposed TPPBA-VP is superior than the compared algorithms in terms of reducing the sum of the energy consumption and tasks' completion time of UAVs.

关键词： workflow scheduling   energy optimization   computing offload   mobile edge computing   multiple resources  

摘要： The energy consumption optimization of edge devices in the mobile edge computing environment is mainly based on computational offload strategy. Most of the current common computing offload strategies only consider a single computing resource and do not comprehensively consider different kinds of computing resources in mobile edge computing environments, which cannot fully reduce the energy consumption of edge devices under the condition of ensuring response time constraints. To solve this problem, a multi-resource computing unloading energy consumption model is proposed in the mobile edge computing environment, and a new fitness calculation method for evaluating the energy consumption of edge devices is designed. Combined with the workflow management system, a multi-resource computing offloading particle swarm optimization task scheduling algorithm for energy consumption optimization in mobile edge computing is proposed. The algorithm can fully reduce the energy consumption of mobile terminals under the condition of considering the response time constraint. Experiments show that, compared with the existing four algorithms, the task scheduling algorithm corresponding to the new strategy has stable convergence and optimal fitness. Under the constraint of user response time, the energy consumption of edge devices in the task scheduling scheme is better than the other four unloading strategies.

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： Device-to-device communication   Task analysis   Optimization   Servers   Resource management   Energy consumption   Relays   Energy-efficient   social network   mobile edge computing   device-to-device communication   matching theory   Lyapunov optimization  

摘要： The future wireless network will face demands of massive connectivity and intensive computation with the increase of mobile devices. Mobile edge computing (MEC) and Device-to-Device (D2D) have emerged as promising technologies to address the above challenges, and implementing social relationships in D2D-MEC networks can improve the reliability of D2D links. Exploiting these benefits, we investigate the energy-efficient task offloading problem in socially-aware D2D-assisted MEC networks, where the user devices can offload tasks to the nearby device or further forward to the MEC server based on social relationships. Specifically, we design a task partial offloading scheme of joint D2D connection selection, transmit power control and task allocation, to maximize the long-term network utility with considering dynamic system status and random task arrival. First, the social relationship among users is quantified into a social trust matrix. As the formulated socially-aware energy-efficient problem is a long-term stochastic optimization problem that is directly intractable, we thus employ the Lyapunov optimization to transform it into a series of short-term problems, each of which can be solved by the Karush-Kuhn-Tucker method and a pricing-based matching algorithm. Finally, we verify the performance optimality and the long-term network stability through numerical simulations as well as theoretical analysis.

关键词： NOMA   Autonomous aerial vehicles   Decoding   Channel estimation   Task analysis   Resource management   Wireless communication   Mobile edge computing   non-orthogonal multiple access   UAV   reconfigurable intelligence surface  

摘要： In the Internet-of-Things scenarios, unmanned aerial vehicle (UAV), as a popular aerial platform, is calling for ever-increasing computing support. This letter proposes a novel mobile edge computing (MEC) framework for UAV with the assistance of the reconfigurable intelligence surface (RIS), where a UAV offloads the computation tasks to ground access points (APs) with the assistance of an RIS, during which non-orthogonal multiple access (NOMA) scheme is employed. We maximize the UAV's computation capacity by jointly optimizing the reflecting phase shift, communication and computation (2C) resource allocation, decoding order, and UAV's deployment. Specifically, we first derive the reflecting phase shift by invoking the concave-convex procedure (CCCP) method and the semidefinite relaxation technique. Next, we obtain the 2C resource allocation by using the CCCP method. The decoding order and the UAV's deployment are finally solved via proposing a grid search (GS) method. Numerical results demonstrate that: 1) the computation capacity is greatly improved by the design of RIS;2) NOMA scheme outperforms orthogonal multiple access scheme;3) the proposed GS method achieves significant performance gains, as compared with the traditional convex approximation method.