关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) enables to provide relatively rich computing resources in close proximity to mobile users, which enables resource-limited mobile devices to offload workloads to nearby edge servers, and thereby greatly reducing the processing delay of various mobile applications and the energy consumption of mobile devices. Despite its advantages, when a large number of mobile users simultaneously offloads their computation tasks to an edge server, due to the limited computation and communication resources of edge server, inefficiency resource allocation will not make full use of the limited resource and cause waste of resource, resulting in low system performance (the weighted sum of the number of processed tasks, the number of punished tasks, and the number of dropped tasks). Therefore, it is a challenging problem to effectively allocate the computing and communication resources to multiple mobile users. To cope with this problem, we propose a performance-aware resource allocation (PARA) scheme, the goal of which is to maximize the long-term system performance. More specifically, we first build the multiuser resource allocation architecture for computing workloads and transmitting result data to mobile devices. Then, we formulate the multiuser resource allocation problem as a Markova Decision Process (MDP). To achieve this problem, a performance-aware resource allocation (PARA) scheme based on a deep deterministic policy gradient (DDPG) is adopted to derive optimal resource allocation policy. Finally, extensive simulation experiments demonstrate the effectiveness of the PARA scheme.

关键词： Mobile edge computing   Non-orthogonal multiple access   Imperfect channel state information   Offloading outage probability  

摘要： In this paper, a non-orthogonal multiple access (NOMA)-based mobile edge computing (MEC) system is proposed, where paired users (themth user and thenth user) offload tasks to the MEC server under imperfect channel state information (ipCSI) condition. To evaluate the effect on performance under ipCSI condition in NOMA-MEC system, this study derives new exact and asymptotic expressions of the offloading outage probability for two users under ipCSI and perfect channel state information (pCSI) conditions. On the basis of the theoretical derivation, the diversity orders of thenth user under ipCSI and pCSI conditions arezeroandn, respectively, while those for the distant user are allzeros. In addition, we also investigate the system throughput and energy efficiency of NOMA-MEC in delay-limited transmission. Numerical results show that (1) the offloading outage behaviors of NOMA-MEC are better than those of time division multiple address (TDMA), (2) the offloading outage performance of paired users under ipCSI is worse than that under pCSI, and (3) the NOMA-MEC system achieves higher throughput and energy efficiency than does the TDMA-MEC system.

关键词： Mobile edge computing   Intelligent resource allocation   Deep reinforcement learning   Federated learning  

摘要： With the emergence of a large number of computation-intensive and time-sensitive applications, smart terminal devices with limited resources can only run the model training part of most intelligent applications in the cloud, so a large amount of training data needs to be uploaded to the cloud. This is an important cause of core network communication congestion and poor Quality-of-Experience (QoE) of user. As an important extension and supplement of cloud computing, Mobile Edge Computing (MEC) sinks computing and storage resources from the cloud to the vicinity of User Mobile Devices (UMDs), greatly reducing service latency and alleviating the burden on core networks. However, due to the high cost of edge servers deployment and maintenance, MEC also has the problems of limited network resources and computing resources, and the edge network environment is complex and mutative. Therefore, how to reasonably allocate network resources and computing resources in a changeable MEC environment has become a great aporia. To combat this issue, this paper proposes an intelligent resource allocation model "DRL + FL". Based on this model, an intelligent resource allocation algorithm DDQN-RA based on the emerging DRL algorithm framework DDQN is designed to adaptively allocate network and computing resources. At the same time, the model integrates the FL framework with the mobile edge system to train DRL agents in a distributed way. This model can well solve the problems of uploading large amounts of training data via wireless channels, Non-IID and unbalance of training data when training DRL agents, restrictions on communication conditions, and data privacy. Experimental results show that the proposed "DRL + FL" model is superior to the traditional resource allocation algorithms SDR and LOBO and the intelligent resource allocation algorithm DRLRA in three aspects: minimizing the average energy consumption of the system, minimizing the average service delay, and balancing resource

关键词： Mobile edge computing   Vocal music   System resources   Resource allocation   Nash equilibrium  

摘要： How to allocate vocal teaching system resources plays an important role in improving the performance of vocal teaching systems. With the development of mobile edge computing, the calculation and storage of resource data can be adapted to the operating needs of vocal teaching systems. This study proposes a method of system resource allocation based on power iteration and sets the throughput of the unloading process as an objective function and realizes the optimal allocation of normal power by iterative optimization. At the same time, in view of the low energy efficiency and resource utilization of edge servers, a heterogeneous network based on edge servers is proposed to find the optimal strategy based on ensuring the Nash equilibrium point. In addition, in order to verify the performance of the algorithm in this paper, a comparative experiment is performed by designing simulation experiments. The results show that the method proposed in this paper has certain effects and strong practicability, which can provide theoretical references for subsequent related research.

关键词： Flow classification   Graphics Processing Unit (GPU)   GPU cluster   Mobile edge computing   Software Defined Networking   Software Defined Mobile Edge Computing (SDMEC)  

摘要： Mobile Edge Computing (MEC) provides different storage and computing capabilities within the access range of mobile devices. This moderates the burden of offloading compute/storage-intensive processes of the mobile devices to the centralized cloud data centers. As a result, the network latency is reduced and the quality of service provided for the mobile end users is improved. Different applications benefit from the large-scale deployments of MEC servers. However, the considerable complexity of managing large scale deployments of the sheer number of applications for the millions of mobile devices is a challenge. Recently, Software Defined Networking (SDN) is leveraged to resolve the problem by providing unified and programmable interfaces for managing network devices. Most of the current SDN packet processing services are tightly dependent on the packet classification service. This primary service classifies any incoming packet based on matching a set of specific fields of its header against a flow table. Acceleration of this basic process considerably increases the performance of the SDN-based MEC. In this paper, the hierarchical tree algorithm, which is a packet classification method, is parallelized using popular platforms on a cluster of Graphics Processing Units (GPUs), a cluster of Central Processing Units (CPUs), and a hybrid cluster. The best scenario for the parallel implementation of this algorithm on the CPU cluster is that which combines OpenMP and MPI. In this case, the throughput of the classifier is 4.2 million packets per second (MPPS). On the GPU cluster, two different scenarios have been used. In the first scenario, the global memory is used to store the rules and the Hierarchical-trie of the classifier while in the second scenario we break the filter set in a way that the resulting Hierarchical-trie of each subset could be stored in the shared memory of GPU. According to the results, although the first GPU cluster scenario achieves a throughput of

关键词： Task analysis   Scheduling   Virtual machining   Cloud computing   Scheduling algorithms   Lattices   Mobile edge computing   virtual machines   formal concept analysis   infusion   matching  

摘要： Mobile Edge Computing (MEC) is providing cloud computing capabilities within the radio access networks and offering a new paradigm to liberate the mobile devices from heavy computational workloads. Importantly, MEC can effectively reduce latency, avoid congestion, and prolong the battery lifetime of mobile devices by offloading the computation tasks from the mobile devices to a physically proximal MEC servers. Particularly, Virtual Machines (VMs) scheduling is a critical issue for tasks offloading and computation in MEC. Regarding to the VMs scheduling problem in MEC environmnet, this paper pioneers the use of Formal Concept Analysis (FCA) methodology for identifying the mapping from tasks to VMs. Specifically, the VMs profile and tasks descriptions are initially characterized as the formal contexts, respectively. With the constructed formal contexts, the corresponding formal concepts which refer to the rules set, are then generated. To better infuse the rules set of VMs and tasks, this paper defines a similarity measurement between formal concepts of VMs and tasks. Consequently, the matching problem from a given task to a virtual machine is to return the expected virtual machine according to the principle of maximum similarity degree between formal concepts of virtual machine and task. Extensive simulations are conducted with a real dataset for the validation of feasibility and effectiveness of the proposed approach. Specifically, the proposed approach can significantly reduce the energy consumption around 28 percent comparing to the approach without consideration of energy consumption. Overall, It is demonstrated that FCA-based VMs scheduling is a novel solution for a sustainable VMs scheduling in MEC environment.

关键词： vehicular network   mobile edge computing   resource allocation   task offloading   global optimization  

摘要： The development of vehicular services in Internet of vehicles poses challenges for vehicles with limited computation resources to guarantee the quality of service (QoS) of latencysensitive and massive computation onboard services. Vehicular mobile edge computing (VEC) has emerged as an effective technology to enhance vehicular service quality through offloading onboard computation tasks to mobile edge computing (MEC) servers. MEC technology can reduce task processing latency and data transmission latency through its on-premises feature. However, the deployment of VEC still faces several problems such as lacking rational and effective resource allocation schemes. In order to solve these problems, we provide an optimal resource allocation mechanism in vehicular MEC systems (RAVEC) to minimize the total task processing delay among a set of vehicles in a time slot by using a global optimization perspective. The method considers the computation ability of each MEC server at road side unit (RSU) in a road segment, the mobility of each vehicle and the total offloading latency of a set of vehicles to get a best resource allocation plan and achieve onboard task offloading. Simulation results show that RAVEC demonstrates a reliable solution and has a certain value for future research.

关键词： Resource management   Servers   Delays   Edge computing   Computational modeling   Computer architecture   Mobile edge computing   software-defined ultra-dense networking   edge resource sharing   design structure matrix  

摘要： The emerging mobile edge computing (MEC) evolutionarily extends the cloud services to the network edge. In order to efficiently coordinate distributed edge resources, software defined networking (SDN) at the network edge has been explored to realize the integrated management of communication, computation, and cache (3C) resources. However, many research efforts, in software-defined edge networks, are mainly devoted to 1C or 2C resource sharing. Motivated by high service performance and user demands, we propose a user-centric edge resource sharing model for software-defined ultra-dense network (SD-UDN) where multiple MEC servers around small base stations (SBSs) can share their 3C resources through OpenFlow-enabled switches. In particular, the service models of MEC servers and users are formulated to optimize the service process by minimizing the service delay, which is NP-hard. To address this NP-hard issue, a service association model is constructed based on design structure matrix (DSM), and a simulated annealing algorithm is employed to further optimize the service association model for reducing time complexity and offering a nearoptimal solution. Compared with traditional 1C or 2C resource sharing, the proposed edge resource sharing model can guarantee lower service delay for users.

关键词： multi-access edge computing   personal multi-access edge computing   multi-access edge computing mobility management   mobile edge computing   mobile edge computing mobility management  

摘要： The Multi-Access Edge Computing (MEC) paradigm provides a promising solution to solve the resource-insufficiency problem in user mobile devices by offloading computation-intensive and delay-sensitive computing services to nearby edge nodes. However, there is a lack of research on the efficient task offloading and mobility support when mobile users frequently move in the MEC environment. In this paper, we propose the mobile personal MEC architecture that utilizes a user's mobile device as an MEC server (MECS) so that mobile users can receive fast response and continuous service delivery. The results show that the proposed scheme reduces the average service delay and provides efficient task offloading compared to the existing MEC scheme. In addition, the proposed scheme outperforms the existing MEC scheme because the existing mobile user devices are used as MECS, enabling low-latency service and continuous service delivery, even as the mobile user requests and task sizes increase.

关键词： Task analysis   Servers   Delays   Wireless communication   Computational modeling   Optimization   Edge computing   Internet of Things (IoT)   network association   event-triggered   Markov decision process (MDP)   Lyapunov optimization   distributed computing   machine learning   mobile edge computing  

摘要： Ultra-low latency communication for mobile intelligent machines, such as autonomous vehicles and robots, is a central technology in Internet of Things (IoT) to achieve system reliability. Proactive network association and communication has been suggested to achieve ultra-low latency under the assistance of mobile edge computing. Highly dynamic and stochastic nature of IoT mobile machines suggests applying machine learning methodology to effectively enhance the proactive network association. In this paper, an online proactive network association is proposed for this distributed computing and networking scenario, in order to minimize the average task delay subject to time-average energy consumption. We first formulate an event-triggered delay model for mobility-aware anticipatory network association mechanism that takes future possible handovers into account. Based on the Markov decision processes (MDP) and Lyapunov optimization, a two-stage online decision algorithm for proactive network association is innovated for individual mobile machine without the statistical knowledge of random events that may lack of enough prior data. Theoretical analysis proves that the delay performance of proposed algorithm attains asymptotic optimality within the bounded deviation. Furthermore, an asynchronous online distributed association decision algorithm based on the nonlinear problem transformation is proposed to support more general scenarios of multi-machine event-triggered associations. Simulations verify the effectiveness of the proposed methodology.