关键词： Mobile Edge Computing   Internet of Things   Service Function Chain   Column   Column Generation   Network Function Virtualization  

摘要： The dynamic network state and the mobility of the terminals make the service function chain (SFC) orchestration mechanisms based on static and deterministic assumptions hard to be applied in SDN/NFV mobile edge computing networks. Designing dynamic and online SFC orchestration mechanism can greatly improve the execution efficiency of compute-intensive and resource-hungry applications in mobile edge computing networks. In order to increase the overall profit of service provider and reduce the resource cost, the system running time is divided into a sequence of time slots and a dynamic orchestration scheme based on an improved column generation algorithm is proposed in each slot. Firstly, the SFC dynamic orchestration problem is formulated as an integer linear programming (ILP) model based on layered graph. Then, in order to reduce the computation costs, a column generation model is used to simplify the ILP model. Finally, a two-stage heuristic algorithm based on greedy strategy is proposed. Four metrics are defined and the performance of the proposed algorithm is evaluated based on simulation. The results show that our proposal significantly provides more than 30% reduction of run time and about 12% improvement in service deployment success ratio compared to the Viterbi algorithm based mechanism.

关键词： Mobile edge computing  

摘要： With the rapid development of mobile communication technology, there is a growing demand for high-quality point of interest(POI) recommendation. The POIs visited by users only account for a very small proportion. Thus traditional POI recommendation method is vulnerable to data sparsity and lacks a clear and effective explanation for POI ranking result. The POI selection made by the user is influenced by various contextual attributes. The challenge lies in representing accurately and aggregating multiple contextual information efficiently. We transform the POI recommendation into a contextual multi-attribute decision problem based on the neutrosophic set (NS) which is suitable for representing fuzzy decision information. We establish a unified framework of contextual information. Firstly, we propose a contextual multi-attribute NS transformation model of POI, including the NS model for single-dimensional attributes and the NS model for multi-dimensional attributes. And then through the aggregation of multi attribute NS, the POI that best conforms to user’s preferences is recommended. Finally, the experimental results based on the Yelp dataset show that the proposed strategy performs better than the typical POI recommendation method in NDCG, accuracy, and recall rate. © 2021, CC BY.

关键词： Mobile edge computing  

摘要： Federated Learning (FL) is a decentralized machine learning architecture, which leverages a large number of remote devices to learn a joint model with distributed training data. However, the system-heterogeneity is one major challenge in a FL network to achieve robust distributed learning performance, which comes from two aspects: i) device-heterogeneity due to the diverse computational capacity among devices;ii) data-heterogeneity due to the non-identically distributed data across the network. Prior studies addressing the heterogeneous FL issue, e.g., FedProx, lack formalization and it remains an open problem. This work first formalizes the system-heterogeneous FL problem and proposes a new algorithm, called FedLGA, to address this problem by bridging the divergence of local model updates via gradient approximation. To achieve this, FedLGA provides an alternated Hessian estimation method, which only requires extra linear complexity on the aggregator. Theoretically, we show that with a device-heterogeneous ratio ρ, FedLGA achieves convergence rates on non-i.i.d. distributed FL training data for the nonconvex optimization problems with O ((1+p)/√ENT + 1/T) and O ((1+p)/√E//TK + 1/T) for full and partial device participation respectively, where E is the number of local learning epoch, T is the number of total communication round, N is the total device number and K is the number of selected device in one communication round under partially participation scheme. The results of comprehensive experiments on multiple datasets show that FedLGA outperforms current FL methods against the system-heterogeneity. Copyright © 2021, The Authors. All rights reserved.

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： In this paper, we propose a novel algorithm for energy-efficient, low-latency dynamic mobile edge computing (MEC), in the context of beyond 5G networks endowed with Reconfigurable Intelligent Surfaces (RISs). In our setting, new computing requests are continuously generated by a set of devices and are handled through a dynamic queueing system. Building on stochastic optimization tools, we devise a dynamic learning algorithm that jointly optimizes the allocation of radio resources (i.e., power, transmission rates, sleep mode and duty cycle), computation resources (i.e., CPU cycles), and RIS reflectivity parameters (i.e., phase shifts), while guaranteeing a target performance in terms of average end-to-end (E2E) delay. The proposed strategy is dynamic, since it performs a low-complexity optimization on a per-slot basis while dealing with time-varying radio channels and task arrivals, whose statistics are unknown. The presence and optimization of RISs helps boosting the performance of dynamic MEC, thanks to the capability to shape and adapt the wireless propagation environment. Numerical results assess the performance in terms of service delay, learning, and adaptation capabilities of the proposed strategy for RIS-empowered MEC. 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： 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： 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.