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： blockchain   data access and sharing   data security   mobile edge computing   privacy   smart contract  

摘要： Mobile edge computing (MEC) integrates mobile and edge computing technologies to provide efficient computing services with low latency. It includes several Internet of Things (IoT) and edge devices that process the user data at the network's edge. The architectural characteristic of MEC supports many internet-based services, which attract more number of users, including attackers. The safety and privacy of the MEC environment, especially user information is a significant concern. A lightweight accessing and sharing protocol is required because edge devices are resource constraints. This paper addresses this issue by proposing a blockchain-enabled security management framework for MEC environments. This approach provides another level of security and includes blockchain security features like temper resistance, immutable, transparent, traceable, and distributed ledger in the MEC environment. The framework guarantees secure data storage in the MEC environment. The contributions of this paper are twofold: (1) We propose a blockchain-enabled security management framework for MEC environments that address the security and privacy concerns, and (2) we demonstrate through simulations that the framework has high performance and is suitable for resource-constrained MEC devices. In addition, a smart contract-based access and sharing mechanism is proposed. Our research uses a combination of theoretical analysis and simulation experiments to demonstrate that the proposed framework offers high security, low latency, legitimate access, high throughput, and low operations cost.

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： 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： 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： 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： 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： Mobile edge computing   microservice   Internet of Vehicles   service placement  

摘要： The integration of Mobile Edge Computing (MEC) and microservice architecture drives the implementation of the sustainable Internet of Vehicles (IoV). The microservice architecture enables the decomposition of a service into multiple independent, fine-grained microservices working independently. With MEC, microservices can be placed on Edge Service Providers (ESPs) dynamically, responding quickly and reducing service latency and resource consumption. However, the burgeoning of IoV leads to high computation and resource overheads, making service resource requirements an imminent issue. What's more, due to the limited computation power of ESPs, they can only host a few services. Therefore, ESPs should judiciously decide which services to host. In this paper, we propose a Microservice-oriented Service Placement (MOSP) mechanism for MEC-enabled IoV to shorten service latency, reduce high resource consumption levels and guarantee long-term sustainability. Specifically, we formulate the service placement as an integer linear programming program, where service placement decisions are collaboratively optimized among ESPs, aiming to address spatial demand coupling, service heterogeneity, and decentralized coordination in MEC systems. MOSP comprises an upper layer to map the service requests to ESPs and a lower layer to adjust the service placement of ESPs. Evaluation results show that the microservice-oriented service deployment mechanism offers dramatic improvements in terms of resource savings, latency reduction, and service speed.

关键词： Mobile edge computing   multi-agent system   task offloading   cooperative edge computing   deep reinforcement learning  

摘要： Driven by the prevalence of the computation-intensive and delay-intensive mobile applications, Mobile Edge Computing (MEC) is emerging as a promising solution. Traditional task offloading methods usually rely on centralized decision making, which inevitably involves a high computational complexity and a large state space. However, the MEC is a typical distributed system, where the edge servers are geographically separated, and independently perform the computing tasks. This fact inspires us to conceive a distributed cooperative task offloading system, where each edge server makes its own decision on how to allocate local computing resources and how to migrate tasks among the edge servers. To characterize diverse task requirements, we divide the arrival tasks into different priorities according to the tolerance time, which enables to dynamically schedule the local computing resources for reducing the task timeout. In order to coordinate the independent decision makings of geographically separate edge servers, we propose a priority driven cooperative task offloading algorithm based on multi-agent deep reinforcement learning, where the decision making of each edge server not only depends on its own state but also on the shared global information. We further develop a Variational Recurrent Neural Network (VRNN) based global state sharing model which significantly reduces the communication overhead among edge servers. The performance evaluation conducted on a movement trajectories dataset of mobile devices verifies that the proposed algorithm can reduce the task consumption time and improve the edge computing resources utilization.

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.