关键词： Mobile edge computing  

摘要： Mobile Edge Computing (MEC) is a key technology of Internet of things (IoTs) and 5G networks and presents many challenges, such as resource discovery, resource allocation, computation offloading and transmission power design that need to be addressed in order to reduce the energy consumption. Previous works studying on improving the performance of MEC system may only optimize these issues separately, or just jointly optimize part of them. In this paper, we propose a resource Discovery-Allocation-Transmission-Offloading (DATO) strategy by taking all these aspects into consideration with discovery order to minimize the energy consumption. The discovery strategy specifies when to stop resource discovery and perform computation offloading. The resource allocation strategy indicates where should the task be executed. The transmission strategy specifies the optimal power used for data transmission. The offloading strategy tells the optimal portion of data offloaded to the server for execution. The discovery order demonstrates the sequence of resource searching. This problem is formulated as a stochastic sequential decision-making problem and Dynamic Programming (DP) is used to obtain the optimal strategy. Furthermore, we study the joint design of DATO strategy and discovery order. The performance of our proposed strategy is evaluated using simulation results. It is found that the energy consumption can be reduced by jointly design of resource discovery, computation offloading and transmission power selection. Besides, adaptive power selection is more important than adaptive offloading ratio. © 2021, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Federated edge learning (FEEL) emerges as a privacy-preserving paradigm to effectively train deep learning models from the distributed data in 6G networks. Nevertheless, the limited coverage of a single edge server results in an insufficient number of participating client nodes, which may impair the learning performance. In this paper, we investigate a novel FEEL framework, namely semi-decentralized federated edge learning (SD-FEEL), where multiple edge servers collectively coordinate a large number of client nodes. By exploiting the low-latency communication among edge servers for efficient model sharing, SD-FEEL incorporates more training data, while enjoying lower latency compared with conventional federated learning. We detail the training algorithm for SD-FEEL with three steps, including local model update, intra-cluster, and inter-cluster model aggregations. The convergence of this algorithm is proved on non-independent and identically distributed data, which reveals the effects of key parameters and provides design guidelines. Meanwhile, the heterogeneity of edge devices may cause the straggler effect and deteriorate the convergence speed of SD-FEEL. To resolve this issue, we propose an asynchronous training algorithm with a staleness-aware aggregation scheme, of which, the convergence is also analyzed. The simulations demonstrate the effectiveness and efficiency of the proposed algorithms for SD-FEEL and corroborate our analysis. Copyright © 2021, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： To overcome long propagation delays for data exchange between the remote cloud data center and end devices in Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC) is emerging to push mobile computing, network control and storage to the network edges. A cloudlet in MEC is a mobility-enhanced small-scale cloud, which contains several MEC servers located in close proximity to mobile devices. The main purpose of a cloudlet is to stably provide services to mobile devices with low latency. When a cloudlet offers hundreds kinds of services to millions of mobile devices, it is critical to balance the loads so as to improve performance. In this paper, we propose a three-layer mobile hybrid hierarchical P2P (MHP2P) model as a cloudlet. MHP2P performs satisfactory service lookup efficiency and system scalability as well as high stability. More importantly, a load balance scheme is provided so that the loads of MHP2P can be well balanced with the increasing of MEC servers and query load. A large number of simulation experiments indicate that the proposed scheme is efficient for enhancing the load balancing performance in MHP2P based MEC systems. Copyright © 2021, The Authors. All rights reserved.

关键词： Internet of Things   mobile edge computing   computing offload   service migration   container  

摘要： Mobile edge computing (MEC) provides users with low-latency, high-bandwidth, and high-reliability services by migrating the computing power of the cloud computing center to the edge of the network. It is thus being considered an effective solution for the contradiction between the limited computing capabilities of Internet of Things (IoT) devices and the rapid development of delay-sensitive real-time applications. In this study, we propose and design a container union file system based on the differencing hard disk and dynamic loading strategy to address the excessively long migration time caused by the bundling transmission of the file system and container images during container-based service migration. The proposed method involves designing a mechanism based on a remote dynamic loading strategy to avoid the downloading of all container images, thereby reducing the long preparation time before which stateless migration can begin. Furthermore, in view of the excessive latency of the edge service during the stateful migration process, a strategy for avoiding the transmission of the underlying file system and container images is designed to optimize the service interruption time and service quality degradation time. Experiments show that the proposed method and strategy can effectively reduce the migration time of container-based services.

关键词： Mobile edge computing   tasks scheduling   security   heuristics  

摘要： The computing power of mobile devices is too limited to execute computation tasks fast. Mobile edge computing (MEC) allows mobile devices to offload tasks to near servers to reduce the completion time of the tasks (a.k.a makespan). The input data of some critical tasks should be encrypted before the offloading. Aiming at the security critical tasks in the MEC composed of multiple servers, this paper addresses to minimize the makespan by scheduling security-critical tasks. We provide the formulation of the problem which is generally an integer programming problem, and three effective composite heuristics CH1-CH3 are proposed to solve the problem. Task permutations are considered as solutions. We construct a greedy heuristic algorithm to calculate the value of the objective. These three composite heuristics consist of two phases: solution initialization and solution improvement. In the first phase, the solutions of all the proposed algorithms are generated by a task arrangement rule called Biggest data Task First (BTF), and then in the second phase, improved by three searching methods based on different neighborhoods including a insertion neighborhood, a swap neighborhood and a hybrid neighborhood, respectively. Experimental results show that CH1-CH3 outperform the well-known RoundRobin algorithm. Particularly, BTF is demonstrated to initialize highly qualified solutions, making contributions to the high effectiveness. Meanwhile, all the improvement methods are justified to be effective and the method based on the hybrid neighborhood achieves the best effectiveness.

关键词： Mobile edge computing  

摘要： Based on mobile edge computing and user perception technology, this paper analyzes and discusses the respective advantages and disadvantages of the important optimization models and mobile models in the animation art design, as well as the wireless block data transmission mechanism and protocol. In order to solve the problem that user mobility cannot be sensed, a content-centric mobile edge animation art design mechanism based on user mobility perception is proposed. This mechanism comprehensively calculates the centrality of users' perception of nodes, the idle rate of animation design, and the staying time of users in a small area. The mobile edge network controller integrates the information of each edge user's perception node, calculates the importance of each edge user's perception node and prioritizes it, and selects the appropriate content animation to design the user perception node according to the ranking result. Finally, various simulation or platform test experiments were carried out for all the design schemes in this paper, and the experimental results were analyzed. The simulation experiment results show that compared with the traditional animation design mechanism, the animation art design system effectively reduces the average number of hops for users to obtain content by up to 15.9%, improves the hit rate of edge user perception node animation design by at least 13.7%, and reduces the traffic entering the core network by up to 32.1%. According to the comparison results, the various designs in this work can successfully use sensor data to preclassify migration tasks in the mobile edge network environment. Compared with the latest block data transmission protocol, it has a significant performance improvement, reducing the data distribution delay by 34.8%, thereby helping to improve the overall efficiency of mobile edge computing.

关键词： Mobile edge computing  

摘要： Federated edge learning (FEEL) has drawn much attention as a privacy-preserving distributed learning framework for mobile edge networks. In this work, we investigate a novel semi-decentralized FEEL (SD-FEEL) architecture where multiple edge servers collaborate to incorporate more data from edge devices in training. Despite the low training latency enabled by fast edge aggregation, the device heterogeneity in computational resources deteriorates the efficiency. This paper proposes an asynchronous training algorithm for SD-FEEL to overcome this issue, where edge servers can independently set deadlines for the associated client nodes and trigger the model aggregation. To deal with different levels of staleness, we design a staleness-aware aggregation scheme and analyze its convergence performance. Simulation results demonstrate the effectiveness of our proposed algorithm in achieving faster convergence and better learning performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Edge node cooperation   Forwarding group   Caching hit ratio   Mobile edge computing   ADMM  

摘要： In this study, we investigate the cooperation problem of edge nodes in mobile edge scenarios. We study two performance metrics: caching hit ratio and cooperation cost. We analyze the relationship of the two metrics for cooperative edge networks in which multiple edge nodes form a forwarding group to serve users. We construct a spanning tree to organize the forwarding group, and maximize the hit ratio subject to a budget constraint on cooperation cost. We first address the optimization problem using the method of Lagrangian multipliers and derive the optimal group parameter. Furthermore, a distributed optimization algorithm with alternating direction method of multipliers (ADMM) via constraint projection and variable splitting is proposed to achieve the desired goal. Finally, we build an edge computing simulation platform consisting of hundreds of edge nodes and users as a case study to verify the effectiveness and efficiency of our proposed method. Numerical results show that it significantly improves the caching hit ratio up to 60%, compared to the classic scheme.

关键词： Mobile Edge Computing   Popularity Prediction   Cooperative caching strategy   Branch and Bound algorithm  

摘要： Mobile edge computing effectively reduces the network delay of requests during data transmission, reduces the transmission burden of data traffic in the network, and improves the quality of mobile services. However, the introduction of mobile edge computing architecture increases the cost of the actual network infrastructure deployment, which increases the complexity of resource management. An edge network architecture based on software-defined network technology is proposed, in which the SDN controller with the entire network state can manage data transmission more intelligently to maximize the utilization of edge servers. In addition, to solve the problems of high request delay and high operating cost in current caching strategies based on video services, a video caching strategy for mobile edge computing is proposed. First, we analyze the user's request data and use the neural network model to predict the content of the user's subsequent time slice request and pre-cache the user's request. Then, improve the quality of user experience and choose the most appropriate edge node cache deployment plan. Finally, a video caching strategy for mobile edge computing with coordinated optimization of delay and energy consumption is proposed. The Branch and Bound algorithm is used to solve the optimization problem. Finally, we compared our algorithm with the LFU algorithm, PBC algorithm, COC algorithm, and R-LCCA algorithm. Experimental results show that the algorithm has a high cache hit rate, thereby reducing the cost of video providers and improving the quality of user experience. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Mobile edge computing   Mobility   Edge service   Service recommendation   Quality of service(QoS)  

摘要： With the proliferation of smartphones and an increasing number of services provisioned by clouds, mobile edge computing (MEC) is emerging as a complementary technology of cloud computing. It could provide cloud resources and services by local mobile edge servers, which are normally nearby users. However, a significant challenge is aroused in MEC because of the mobility of users. User trajectory prediction technologies could be used to cope with this issue, which has already played important roles in service recommendation systems with MEC. Unfortunately, little attention and work have been given in service recommendation systems considering users mobility. Thus, in this paper, we propose a mobility-aware personalized service recommendation (MPSR) approach based on user trajectory and quality of service (QoS) predictions. In the proposed method, users trajectory is firstly discovered by a hybrid long-short memory network. Then, given users trajectories, service QoS is predicted, considering the similarity of different users and different edge servers. Finally, services are recommended by a center trajectory strategy through MPSR. Experimental results on a real dataset show that our proposed approach can outperform the traditional recommendation approaches in terms of accuracy in mobile edge computing.