关键词： Mobile edge computing   Service migration   Three-way decisions   Evaluation function   Mobile edge computing   Service migration   Three-way decisions   Evaluation function  

摘要： In mobile edge computing (MEC), the mobile device cannot always stably connect with the same edge server due to the movement of the user. Considering the complexity and ran-domness of users' movement, most of the existing service migration strategies based on two-way decisions (namely either migrate or not migrate) may make wrong decisions, which will increase the energy consumption and significantly impact users' experience. To address such an issue, we employ three-way decisions for service migration. Specifically, first, based on users' movement trajectory, we allocate users into three regions: migration region, non-migration region and delay region. Afterwards, we execute different operations for users in different regions accordingly. In the migration region, con-sidering the energy consumption and service delay time, we propose a new service migra-tion method which utilizes the latest migration radius and the lowest energy consumption for service migration. In the delay region, the edge server will keep collecting users' move-ment information to prepare for the further decision making. In the non-migration region, no action is required by the edge server. Comprehensive simulation experiments using parameter settings consistent with real-world edge computing environments are con-ducted to show its superior performance compared with other strategies.(c) 2022 Published by Elsevier Inc.

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.

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： Computation offloading   Mobile edge computing   Code analysis   IoT application  

摘要： Mobile Edge Computing (MEC) provides a new opportunity to reduce the latency of IoT applications significantly. It does so by offloading computation-intensive tasks in applications from IoT devices to mobile edges, which are located N-close proximity to the IoT devices. However, the prior researches focus on supporting computation offloading for a specific type of applications. Meanwhile, making multi-task and multi-server offloading decisions in highly complex and dynamic MEC environments remains intractable. To address this problem, this paper proposes a novel approach called MultiOff. First, we propose a generic program structure that supports on-demand computation offloading. Applications conforming to this structure can extract the flowcharts of program fragments via code analysis. Second, a novel cost-efficient offloading strategy based on a Multi-task Particle Swarm Optimization algorithm using the Genetic Algorithm operators (MPSO-GA) is proposed. MPSO-GA makes offloading decisions by analyzing program fragment flowcharts and context. Finally, each application can be offloaded at the granularity of services with the offloading scheme, minimizing the system cost while satisfying the deadline constraint for each application. We evaluate MultiOff on several real-world applications and the experimental results show that MultiOff can support computation offloading for different types of applications at the fine-grained granularity of services. Moreover, MPSO-GA can save about 2.11-17.51% system cost compared with other classical methods while meeting time constraints.

关键词： Mobile edge computing   Unmanned aerial vehicle   Differential evolution   Clustering algorithm   Tabu search algorithm  

摘要： This paper presents an energy and task completion time minimization scheme for the unmanned aerial vehicles (UAVs)-empowered mobile edge computing (MEC) system, where several UAVs are deployed to serve large-scale users' equipment (UEs). The aim is to minimize the weighted sum of energy consumption and task completion time of the system by planning the trajectories of UAVs. The problem is NP-hard, non-convex, non-linear, and mixed-decision variables. Therefore, it is very challenging to be solved by conventional optimization techniques. To handle this problem, this paper proposes an energy and task completion time minimization algorithm (ETCTMA) that solves the above problem in three steps. In the first step, the deployment updation of stop points (SPs) is handled by adopting a differential evolution algorithm with a variable population size. Then, in the second step, the association between SPs and UAVs is determined. Specifically, a clustering algorithm is proposed to associate SPs with UAVs. Finally, in the third step, a low-complexity tabu search algorithm is adopted to construct the trajectories of all UAVs. The performance of the proposed ETCTMA is tested on seven instances with up to 700 UEs. The results reveal that our proposed algorithm ETCTMA outperforms other variants in terms of minimizing the weighted sum of energy consumption and task completion time of the system.

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： DNN service migration   Multi-exit DNN   Service downtime   Mobile edge computing   Model predictive control  

摘要： Edge intelligence (EI) becomes a trend to push the deep learning frontiers to the network edge, so that deep neural networks (DNNs) applications can be well leveraged at resource-constrained mobile devices with benefits of edge computing. Due to the high user mobility among scattered edge servers in many scenarios such as internet of vehicular applications, dynamic service migration is desired to maintain a reliable and efficient quality of service (QoS). However, inevitable service downtime incurred by service migration would largely degrade the real-time performance of delay-sensitive DNN inference services. To address this issue, we advocate a user-centric management for dynamic DNN inference service migration with flexible multi-exit mechanism, aiming at maximizing overall user utility (e.g., DNN model inference accuracy) with various service downtime. We first leverage dynamic programming to propose an optimal offline migration and exit point selection strategy (OMEPS) algorithm when complete future information of user behaviors is available. Amenable to a more practical application domain without complete future information, we incorporate the OMEPS algorithm into a model predictive control (MPC) framework, then construct a mobility-aware service migration and DNN exit point selection (MOMEPS) algorithm, which improves the long-term user utility within limited predictive future information. However, heavy computation overheads of MOMEPS algorithm impose burdens on mobile devices, thus we further advocate a cost-efficient algorithm, named smart-MOMEPS, which introduces a smart migration judgement based on Neural Networks to control the implementation of (MOMEPS) algorithm by wisely estimating whether the DNN service should be migrated or not. Extensive trace-driven simulation results demonstrate the superior performance of our smart-MOMEPS algorithm for achieving significant overall utility improvements with low computation overheads compared with other online

关键词： Task analysis   Resource management   Energy consumption   Optimization   Wireless communication   Reinforcement learning   Internet of Vehicles   6G   B5G   block coordinate descent   computation offloading   energy efficiency   Internet of Vehicles   mobile edge computing  

摘要： The Internet of Vehicles (IoV) is an emerging paradigm, which is expected to be an integral component of beyond-fifth-generation and sixth-generation mobile networks. However, the processing requirements and strict delay constraints of IoV applications pose a challenge to vehicle processing units. To this end, multi-access edge computing (MEC) can leverage the availability of computing resources at the edge of the network to meet the intensive computation demands. Nevertheless, the optimal allocation of computing resources is challenging due to the various parameters, such as the number of vehicles, the available resources, and the particular requirements of each task. In this work, we consider a network consisting of multiple vehicles connected to MEC-enabled roadside units (RSUs) and propose an approach that minimizes the total energy consumption of the system by jointly optimizing the task offloading decision, the allocation of power and bandwidth, and the assignment of tasks to MEC-enabled RSUs. Due to the original problem complexity, we decouple it into subproblems and we leverage the block coordinate descent method to iteratively optimize them. Finally, the numerical results demonstrate that the proposed solution can effectively minimize total energy consumption for various numbers of vehicles and MEC nodes while maintaining a low outage probability.

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： 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.