关键词： Mobile edge computing   Internet of Things   software defined networks   traffic classification   machine learning   resource allocation  

摘要： Heterogeneous Internet of Things(IoT)applications generate a diversity of novelty applications and services in next-generation networks(NGN),which is essential to guarantee end-to-end(E2E)communication resources for both control plane(CP)and data plane(DP).Likewise,the heterogeneous 5th generation(5G)communication applications,including Mobile Broadband Communications(MBBC),massive Machine-Type Commutation(mMTC),and ultra-reliable low latency communications(URLLC),obligate to perform intelligent Quality-of-Service(QoS)Class Identifier(QCI),while the CP entities will be suffered from the complicated massive HIOT ***,the existing management and orchestration(MANO)models are inappropriate for resource utilization and allocation in large-scale and complicated network *** cope with the issues mentioned above,this paper presents an adopted software-defined mobile edge computing(SDMEC)with a lightweight machine learning(ML)algorithm,namely support vector machine(SVM),to enable intelligent MANO for real-time and resource-constraints IoT applications which require lightweight computation ***,the SVM algorithm plays an essential role in performing QCI ***,the software-defined networking(SDN)controller allocates and configures priority resources according to the SVM classification ***,the complementary of SVM and SDMEC conducts intelligent resource MANO for massive QCI environments and meets the perspectives of mission-critical communication with resource constraint *** on the E2E experimentation metrics,the proposed scheme shows remarkable outperformance in key performance indicator(KPI)QoS,including communication reliability,latency,and communication throughput over the various powerful reference methods.

关键词： Delays   Quality of service   Edge computing   Cloud computing   Switches   Optimization   Mobile computing   Mobile edge computing   network selection   service placement   online algorithm   stable matching   game theory  

摘要： With the rapid development and deployment of 5G wireless technology, mobile edge computing (MEC) has emerged as a new computing paradigm to facilitate a large variety of infrastructures at the network edge to reduce user-perceived communication delay. One of the fundamental problems in this new paradigm is to preserve satisfactory quality-of-service (QoS) for mobile users in light of densely dispersed wireless communication environment and often capacity-constrained MEC nodes. Such user-perceived QoS, typically in terms of the end-to-end delay, is highly vulnerable to both access network bottleneck and communication delay. Previous works have primarily focused on optimizing the communication delay through dynamic service placement, while ignoring the critical effect of access network selection on the access delay. In this work, we study the problem of jointly optimizing the access network selection and service placement for MEC, with the objective of improving the QoS in a cost-efficient manner by judiciously balancing the access delay, communication delay, and service switching cost. Specifically, we propose an efficient online framework to decompose a long-term time-varying optimization problem into a series of one-shot subproblems. To address the NP-hardness of the one-shot problem, we design a computationally-efficient two-phase algorithm based on matching and game theory, which achieves a near-optimal solution. Both rigorous theoretical analysis on the optimality gap and extensive trace-driven simulations are conducted to validate the efficacy of our proposed solution.

关键词： Mobile Edge Computing   Cooperative transmission   Resource optimization   Scalable video   QoE  

摘要： With the continuously increasing demands for video application, high-quality video transmission will still be one of the major challenges for future Mobile Edge Computing (MEC) assisted 5G heterogeneous networks. It poses the pressing need for solutions that enable to optimize the resource allocation. Therefore, we design a complete and practical multipath cooperative transmission scheme for scalable video, to achieve efficient operation of cache management, resource optimization, signaling interaction and information processing. On this basis, an improved cache optimization algorithm is proposed to determine an appropriate cooperative cache strategy for all MEC severs. By considering the structural features of SVC, content-related rate-quality relationship and the delay characteristics of multipath cooperative transmission, it can effectively improve cache hit ratio while decreasing the average delay. For further improving the overall QoE of all clients, a novel transmission resource optimization problem that aims to maximize the sum of the short-time quality gain and the final quality achievement of all the clients, is formulated. To accurately predict the final quality achievement, we establish a low-complexity model for real-time analysis of the end-to-end delay, and subsequently propose an innovative quality prediction method with considering deadline constraint. Based on the formulation, a two-phase algorithm is developed to address the problem efficiently. The simulation results show that our scheme outperforms the others in the caching optimization stage in terms of both hit rate and delay, and moreover achieves the playback continuity while guaranteeing higher video quality in the transmission optimization stage.

关键词： Unmanned aerial vehicle   mobile edge computing   blockchain   security   computation offloading  

摘要： The introduction of mobile edge computing (MEC) technology in unmanned aerial vehicle (UAV) networks can provide computing services for mobile users with or without communication infrastructure coverage. However, mobile users' privacy may be leaked during the computation offloading process due to the information interaction between UAVs and the migration of computation tasks between mobile users and UAVs. To this end, we propose a secure aerial computing architecture that integrates MEC and blockchain technology for UAV networks to effectively ensure the security and privacy of computation offloading between UAVs and mobile users. Under this architecture, a problem of joint optimization of user association, UAV trajectory, block processor scheduling, and computation resource allocation is formulated to minimize the weighted sum of the energy consumption and the delay in completing computation tasks and blockchain tasks processing. To handle this intractable issue, we first decouple the optimization variables and then separate the original problem into multiple subproblems to be solved alternately. In addition, we design a block coordinate descent (BCD)-based algorithm for user association and computation resource allocation, and a successive convex approximation (SCA)-based algorithm to optimize the trajectories of UAVs. Simulation results show that the proposed algorithm has better performance.

关键词： Mobile edge computing   parked vehicle   reliable service   task offloading   resource allocation  

摘要： Nowadays, a growing number of computation-intensive applications appear in our daily life. Those applications make the loads of both the core network and the mobile devices, in terms of energy and bandwidth, hugely increase. Offloading computation-intensive tasks to edge cloud is proposed to address this issue. Since edge clouds have limited computation resources compared with the remote cloud, they would get over-loaded because of the heavy computation burden. Parked-vehicle-assisted mobile edge computing becomes one of the promising solutions for this problem. However, several critical issues in parked-vehicle-assisted mobile edge computing would result in low reliable edge service. The open environment would bring about uncertainty, and the data privacy is hard to ensure. In addition, different from edge cloud, each parked vehicle only has limited parking duration and can leave unexpectedly for personal reasons. Moreover, edge cloud and vehicle adopt different execution models of computation and communication. The heterogeneous environment may result in negative effect on cooperativeness. Ignoring those issues can result in substantial performance degradation. To tackle this challenge and explore the benefits of parked-vehicle-assisted offloading, we study the task offloading and resource-allocation problem by fully considering the above issues. First, we propose a resource-management scheme to address the privacy issue. Second, we review the execution model of computation and communication in parked-vehicle-assisted computation offloading. Then, we formulate the problem into a mixed-integer nonlinear programming. The problem is hard to tackle due to its non-convex nature, which means that the time complexity of finding global optimal solution is unaffordable. Finally, we decompose the original problem into two sub-problems with lower complexity, and related algorithms are given to deal with the sub-problems. Simulation results demonstrate the effectiveness of th

关键词： aerial network   mobile edge computing   6G   computation offloading  

摘要： In the 6G aerial network, all aerial communication nodes have computing and storage functions and can perform real-time wireless signal processing and resource management. In order to make full use of the computing resources of aerial nodes, this paper studies the mobile edge computing (MEC) system based on aerial base stations (AeBSs), proposes the joint optimization problem of computation the offloading and deployment control of AeBSs for the goals of the lowest task processing delay and energy consumption, and designs a deployment and computation offloading scheme based on federated deep reinforcement learning. Specifically, each low-altitude AeBS agent simultaneously trains two neural networks to handle the generation of the deployment and offloading strategies, respectively, and a high-altitude global node aggregates the local model parameters uploaded by each low-altitude platform. The agents can be trained offline and updated quickly online according to changes in the environment and can quickly generate the optimal deployment and offloading strategies. The simulation results show that our method can achieve good performance in a very short time.

关键词： Mobile edge computing   computation offloading   service replication   lagrangian analysis  

摘要： The number of individuals and groups of users offloading independent and inter-related computational tasks to mobile edge computing (MEC) servers is rapidly increasing, thus overloading them and raising the risk of service interruptions. Hence, reactive service replication has been suggested to enable individuals and groups of users to access services from remote edge servers, thus guaranteeing system scalability. This paper proposes a task offloading and service replication scheme on local and remote MEC servers. The scheme minimizes the response time of all users while satisfying the delay requirements of user groups in traffic-heavy and multimedia-intense applications (e.g., online gaming, multimedia conferencing, augmenting reality). We formulate an integer linear problem that minimizes the average response time of all users while satisfying the time and time difference constraints of the user groups running the same applications. We then use linear relaxation programming using Lagrangian analysis and solve the problem using a numerical solver. In addition, we compare the optimal solution to distance-based and resource-based greedy approaches. The results demonstrate the merits of our proposed optimized decision scheme compared to these two greedy approaches.

关键词： Mobile edge computing   meta distribution   stochastic geometry  

摘要： The stochastic geometry modeling and analysis of mobile edge computing (MEC) networks contribute to the effective network configuration. Most prior works investigate the MEC network performance by spatial averaging the topological randomness, which can hide the differences among users and result in inaccurate analytical results. To address this limitation, an analytical framework is developed to study the meta distribution of the task offloading performance. We consider the Poisson MEC network where the users with dynamic task requests offload the tasks to the MEC server. Given the model, the b-th moment and the meta distribution of the success task offloading probability are first analyzed. Further, the exact yet convenient approximation of the MEC computing load is derived, based on which the edge computing delay is obtained. Our results demonstrate that the proposed meta-analytical framework allows tractable yet accurate analytical results, and provides additional fine-grained information about MEC-network planning.

关键词： mobile edge computing   intelligent reflecting surfaces   latency   optimization  

摘要： Intelligent reflecting surfaces (IRS) and mobile edge computing (MEC) have recently attracted significant attention in academia and industry. Without consuming any external energy, IRS can extend wireless coverage by smartly reconfiguring the phase shift of a signal towards the receiver with the help of passive elements. On the other hand, MEC has the ability to reduce latency by providing extensive computational facilities to users. This paper proposes a new optimization scheme for IRS-enhanced mobile edge computing to minimize the maximum computational time of the end users' tasks. The optimization problem is formulated to simultaneously optimize the task segmentation and transmission power of users, phase shift design of IRS, and computational resource of mobile edge. The optimization problem is non-convex and coupled on multiple variables which make it very complex. Therefore, we transform it to convex by decoupling it into sub-problems and then obtain an efficient solution. In particular, the closed-form solutions for task segmentation and edge computational resources are achieved through the monotonical relation of time and Karush-Kuhn-Tucker conditions, while the transmission power of users and phase shift design of IRS are computed using the convex optimization technique. The proposed IRS-enhanced optimization scheme is compared with edge computing nave offloading, binary offloading, and edge computing, respectively. Numerical results demonstrate the benefits of the proposed scheme compared to other benchmark schemes.

关键词： Big data   IoT   Mobile edge computing   Deep learning   Feature selection  

摘要： In present times, a massive quantity of big data has been generated by the Internet of Things (IoT) devices for a wide range of applications. The IoT devices generate an enormous data quantity that is troublesome for data processing and analytics functionalities, which is effortlessly managed by the cloud before the explosive development of the IoT. Specifically, the big IoT data analytics by mobile edge computing (MEC) becomes a hot research topic and needs comprehensive research works for intelligent decision making. This paper introduces a new generative adversarial network (GAN) with a quantum elephant herd optimization (QEHO) algorithm for MEC in IoT enabled big data environment called GAN-QEHO. The presented GAN-QEHO algorithm follows two-stage processes, namely feature selection (FS) and data classification. The QEHO algorithm is used to elect an optimal feature subset for the FS process. By the quantization of elephant individuals, the search scope of feature space can be enhanced, and an optimal tradeoff has been attained among exploration and exploitation. Then, the GAN model is employed for the classification process to identify different class labels. In order to validate the experimental results analysis of the GAN-QEHO algorithm, a series of simulations take place in terms of diverse aspects.