关键词： Mobile edge computing   Dynamic offloading   LSTM   Lyapunov-based algorithm  

摘要： With the increasing popularity of mobile devices and explosive growth of task processing requirements, edge computing attracts more attention from researchers nowadays since it can improve the QoS and utilize resources of the cloudlets, including mobile devices and base stations, as much as possible. In a Mobile Edge Computing (MEC) network, the workload offloading problem is quite important since it directly influences the latency of the task processing, and many efficient algorithms have been proposed to deal with it. However, most of the existing algorithms only consider the static or quasi-static scenario, and are not suitable for a system with several fast-moving devices. Since that more and more mobile devices with high speed are involved in a MEC system, a new workload offloading strategy is required to adapt to such a dynamic scenario. In this paper, we sufficient consider the dynamic properties of a MEC system, and proposed a dynamic adaptive workload offloading algorithm based on Lyapunov theories and an FC-LSTM based schedule determining algorithm to balance the workload of different cloudlets and minimize the weighted average energy and time consumption of mobile devices. Theoretical analysis and extensive experimental results show that all the proposed algorithms achieve high performance in terms of energy consumption, convergence and latency.

关键词： Non-orthogonal multiple access   mobile edge computing   task offloading   B5G   6G  

摘要： To mitigate the computational power gap between the network core and edges, mobile edge computing (MEC) is poised to play a fundamental role in future generations of wireless networks. In this correspondence, we consider a non-orthogonal multiple access (NOMA) transmission model to maximize the worst task to be offloaded among all users to the network edge server. A provably convergent and efficient algorithm is developed to solve the considered non-convex optimization problem for maximizing the minimum number of offloaded bits in a multi-user NOMA-MEC system. Compared to the approach of optimized orthogonal multiple access (OMA), for given MEC delay, power and energy limits, the NOMA-based system considerably outperforms its OMA-based counterpart in MEC settings. Numerical results demonstrate that the proposed algorithm for NOMA-based MEC is particularly useful for delay sensitive applications.

关键词： Federated learning   Artificial intelligence   Incentive mechanism   Mobile edge computing  

摘要： We design a two-stage federated learning incentive mechanism by including both a "combinatorial auction" stage and a "bargaining" stage, referred as Combinatorial Auction and Bargaining (CAB). We demonstrate that the CAB-based federated learning incentive mechanism not only maximizes the overall utility of both the federated learning platform and mobile users, but also generates greater profits for the platform than other mechanisms. Further, we apply both theoretical and numerical analysis to prove the effectiveness of the proposed mechanism and show that it is individually rational and incentive compatible, and outperforms other baseline mechanisms.

关键词： Energy consumption   Energy harvesting   Lyapunov optimization   mobile edge computing   task offloading  

摘要： With the explosive increase of Internet of Things (IoT) devices, an increasing number of computation-intensive applications are emerging in IoT system. However, most IoT devices are limited by size and location, equipped with low-performance CPUs and low-capacity batteries, which cannot go well with computation-intensive applications. Mobile edge computing (MEC) is considered as a promising solution to provide computation-intensive and latency-sensitive services in IoT system, but it is still challenging to improve the throughput and extend the battery life of IoT devices under communication constraints. This paper focuses on the task offloading problem for an MEC system with multiple energy harvesting (EH) devices. To accommodate the system dynamics and ensure the system stability in terms of task queue and battery level, we apply Lyapunov optimization theory, and design a computation tasks maximum offloading algorithm to maximize the system throughput. The algorithm can determine the offloading decision in real-time without knowing any statistical information about the system. We first give a series of mathematical analysis to verify the system stability and discuss the performance of the algorithm. In addition, a number of simulation experiments are conducted to present the efficiency of the algorithm.

关键词： Many-objective optimization   mobile edge computing   resource allocation   task offloading  

摘要： Mobile edge computing (MEC) is an effective computing tool to cope with the explosive growth of data traffic. It plays a vital role in improving the quality of service for user task computing. However, the existing solutions rarely address all the significant factors that impact the quality of service. To challenge this problem, a trusted many-objective model is built by comprehensively considering the task time delay, server energy consumption, trust metrics between task and server, and user experience utility factors in multi-server MEC networks. We decompose the original problem into task offloading (TO) and resource allocation (RA) to address the model. Then a novel hybrid many-objective optimization algorithm based on cascading clustering and incremental learning is designed to optimize the TO decision solutions. Alow-complexity heuristic method is adopted based on the optimal TO decision solutions to optimize the RA problem continuously. To verify the model's validity and the optimisation algorithm's superiority, five other advanced many-objective algorithms are used for comparison. The results show that our algorithm has more than half the number of the superior values for the benchmark problem. And the obtained model solution shows good performance on different indicators metrics for the decomposition problem.

关键词： Containers   Servers   Delays   Trajectory   Optimization   Load management   Prediction algorithms   Mobile edge computing   trajectory prediction   dependency-aware migration   service latency   load balancing   IOV  

摘要： Edge computing and container technologies offer more possibilities for the development of Internet of Vehicles (IOV). However, many studies have neglected the dependencies among containers and the mobility of users. In this article, we propose a container migration strategy based on trajectory prediction, with the consideration of dependencies among containers. Given a set of containers with dependencies, we aim to reduce the service latency while distributing the containers as evenly as possible for load balance. Specifically, we leverage Recurrent Neural Network (RNN) to train a model for trajectory prediction. Based on the prediction, we can identify the location of the vehicles, after which we develop a mathematical model for service delay. We formulate the problem as a 0-1 program and solve the problem by Hunger Games Search (HGS). The proposed algorithm is validated with a real vehicle dataset and an Alibaba cluster dataset. Experiment results demonstrate that we can predict the trajectory of vehicle accurately and the container migration strategy can effectively reduce service latency and improve server load balancing, compared to alternative algorithms. In addition, we characterize the impact of error correction mechanism and also the effect of bandwidth on the optimization.

关键词： artificial intelligence   Internet of Things   machine learning   mobile edge computing   network Management  

关键词： IIoT   mobile edge computing   wired edge devices   MIMO precoding   computation offloading  

摘要： In the fourth industrial revolution, the industrial Internet of Things (IIoT) will bring fundamental changes to human communities. This paper proposes to make full use of the under-utilized computing resources of wired edge devices to alleviate the computing burden of the processing center in delay-constrained multi-user networks. Our goal is to minimize the weighted energy consumption while ensuring the required latency. The formulated problems are NP-hard involving joint optimization of the computation task assignment, transmit association design, multiple-input multiple-output (MIMO) precoding, and computing resource allocation with binary and partial offloading protocols. By utilizing the weighted minimum mean-squared-error method, quadratic transformation, and difference of convex functions algorithm, we propose two joint computation offloading and resource allocation algorithms for binary and partial offloading protocols, respectively. Simulation results confirm the efficiency of the proposed algorithms and demonstrate that the proposed algorithms achieve a significant computation performance enhancement.

关键词： Computation offloading   Internet of Vehicles   mobile edge computing   multi-objective reinforcement learning   radial basis neural networks  

摘要： As a promising computing paradigm driven by 5G, mobile edge computing (MEC) empowers smart vehicles to offload computation-intensive tasks to edge devices in the Internet of Vehicles (IoV), thereby providing a plethora of exciting applications (e.g. on-board AR/VR, autonomous driving, etc.) with the unique quality of service (QoS) guarantees. However, a key challenge of MEC is howto keep the delay and energy consumption to aminimum in the computation offloading process, while ensuring the privacy security of offloaded data and the load balancing of edge resources. Namely, howto simultaneously optimize multiple indicators that affect computation offloading, which creates a challenging multi-objective optimization (MOO) problem. Aiming at the MOO problem, we propose a novel multi-objective reinforcement learning (MORL) algorithm based on double deep Q-network (DDQN). Each DDQN agent obtains rewards on different objectives according to different reward functions, and dynamically approximates the optimal offloading decision on multiple objectives. To conquer the trade-off problem among multiple conflicting objectives, we propose a weight-learning network based on radial basis function (RBF) networks, which dynamically adjusts the weights by learning the value changes among the objectives. Interestingly, we discovered encouraging potential ofMORLfor solving computation offloading problems in IoV, and numerical results show that the proposed algorithm outperforms traditional reinforcement learning methods by 30.24% in terms of overall energy consumption.

关键词： Mobile edge computing   cost-aware   microservices deployment   IoT applications  

摘要： In Mobile Edge Computing (MEC) environment, service deployment for IoT application is a key issue that needs to be solved. Considering the knowledge of mobile users' service requests and edge server's processing capacity, the problem of microservice deployment in MEC environment is modelled as a non-linear optimization problem. An adaptive dynamic deployment optimization method called Adapt-SD has been proposed, which is based on Adam and weighted round-robin scheduling algorithm to solve this microservice deployment problem. In Adapt-SD, considering the hardware resource-constrained MEC environment, different numbers of microservice instances are deployed on different edge servers, and then microservice instances are invoked to achieve the minimum resource consumption cost while meeting user's service access delay constraints. At the same time, Adapt-SD also ensures the work balance of edge servers. In this paper, real datasets from EUA in Australia and some synthetic datasets are utilized to measure the performance of Adapt-SD, which is compared with the existing microservice deployment algorithms. Experimental results show that Adapt-SD is superior to other representative deployment algorithms.