关键词： Mobile edge computing  

摘要： Opportunistic offloading can be utilized to offload computing tasks and traffic data in Mobile Edge Computing (MEC). To improve the ratio of successful data offloading and reduce unnecessary data redundancy in opportunistic forwarding process, some methods of evaluating a device's forwarding capability are proposed. However, most of these methods do not consider the temporal impact from device mobility and the efficiency influence from the capability computation process. To settle these problems, we proposed a Transient-cluster-based Capability Evaluation Method (TCEM) to evaluate a device's data forwarding capability. The TCEM can be divided into two steps. The first step aims to reduce computational complexity by evaluating a device's possibility of contacting the destination within a time constraint based on the transient cluster generated by our proposed Transient Cluster Detection Method (TCDM). The second step is to calculate a device's probability of directly and indirectly forwarding data to the destination. The probability as a metric of evaluating a device's forwarding capability can be used in different data forwarding strategies. Simulation results demonstrate that the TCEM-based data forwarding strategy outperforms other data forwarding strategies from the aspect of the proportion of the data delivery ratio to the data redundancy.

关键词： Internet of Vehicles   social networks   unmanned aerial vehicles   mobile edge computing   resource allocation  

摘要： Social Internet of Vehicles (SIoV) is a new paradigm that enables social relationships among vehicles by integrating vehicle-to-everything communications and social networking properties into the vehicular environment. Through the provision of diverse socially-inspired applications and services, the emergence of SIoV helps to improve the road experience, traffic efficiency, road safety, travel comfort, and entertainment along the roads. However, the computation performance for those applications have been seriously affected by resource-limited on-board units as well as deployment costs and workloads of roadside units. Under such context, an unmanned aerial vehicle (UAV)-assisted mobile edge computing environment over SIoV with a three-layer integrated architecture is adopted in this paper. Within this architecture, we explore the energy-aware dynamic resource allocation problem by taking into account partial computation offloading, social content caching, and radio resource scheduling. Particularly, we develop an optimization framework for total utility maximization by jointly optimizing the transmit power of vehicle and the UAV trajectory. To resolve this problem, an energy-aware dynamic power optimization problem is formulated under the constraint of the evolution law of energy consumption state for each vehicle. By considering two cases, i.e., cooperation and noncooperation among vehicles, we obtain the optimal dynamic power allocation of the vehicle with a fixed UAV trajectory via dynamic programming method. In addition, under the condition of fixed power, a search algorithm is introduced to derive the optimized UAV trajectory based on acceptable ground-UAV distance metric and the optimal offloaded data size of the vehicle. Simulation results are presented to demonstrate the effectiveness of the proposed framework over alternative benchmark schemes.

关键词： Mobile edge computing  

摘要： The progress in realizing the Fifth Generation (5G) mobile networks has been accelerated recently towards deploying 5G prototypes with increasing scale. One of the Key Performance Indicators (KPIs) in 5G deployments is the service deployment time, which should be substantially reduced from the current 90 hours to the target 90 minutes on average as defined by the 5G Public-Private Partnership (5G-PPP). To achieve this challenging KPI, highly automated and coordinated operations are required for the 5G network management. This paper addresses this challenge by designing and prototyping a novel 5G service deployment orchestration architecture that is capable of automating and coordinating a series of complicated operations across physical infrastructure, virtual infrastructure, and service layers over a distributed mobile edge computing paradigm, in an integrated manner. Empirical results demonstrate the superior performance achieved, which meets the 5G-PPP KPI even in the most challenging scenario where 5G services are installed from bare metal.

关键词： Mobile edge computing   offloading   coalitional game   pricing  

摘要： Mobile edge computing (MEC) that affords service to the vicinity of mobile devices (MDs) has become a key technology for future network. Offloading big data to the MEC server for preprocessing is an attractive approach of MDs. In this paper, we investigate data offloading from MDs to MEC servers, and a coalitional game-based pricing scheme is proposed. We apply coalitions to arrange data offloading of MDs as well as depict the offloading relationship between MDs and MEC servers. Furthermore, pricing is utilized as the stimuli for the offloading. In the proposed scheme, a scheduled MD in a coalition chooses an MEC server within the same coalition for offloading, and pays the selected MEC server for the MEC service. By analyzing the formulated coalitional non-transferable utility game, theoretical properties are derived and sufficient conditions for the core existence are given. Finally, the utility performances of the proposed scheme are demonstrated by numerical results.

关键词： Mobile edge computing   heterogeneous networks   offloading strategy   resource allocation   game theory  

摘要： In this paper, we propose a distributed joint computation offloading and resource allocation optimization (JCORAO) scheme in heterogeneous networks with mobile edge computing. An optimization problem is formulated to provide the optimal computation offloading strategy policy, uplink subchannel allocation, uplink transmission power allocation, and computation resource scheduling. The optimization problem is decomposed into two sub-problems due to the NP-hard property. In order to analyze the offloading strategy, a sub-algorithm named distributed potential game is built. The existence of Nash equilibrium is proved. To jointly allocate uplink subchannel, uplink transmission power, and computation resource for the offloading mobile terminals, a sub-algorithm named cloud and wireless resource allocation algorithm is designed. The solutions for subchannel allocation consist of uniform zero frequency reuse method without interference and fractional frequency reuse method based on Hungarian and graph coloring with interference. A distributed JCORAO scheme is proposed to solve the optimization problem by the mutual iteration of the two sub-algorithms. Simulation results show that the distributed JCORAO scheme can effectively decrease the energy consumption and task completion time with lower complexity.

关键词： Mobile edge computing   virtual reality   communications-computing-caching tradeoffs  

摘要： Mobile edge computing (MEC) is expected to be an effective solution to deliver virtual reality (VR) videos over wireless networks. In contrast to previous computation-constrained MEC, which reduces the computation-resource consumption at the mobile device by increasing the communication-resource consumption, we develop a communications-constrained MEC framework to reduce communication-resource consumption by fully exploiting the computation and caching resources at the mobile VR device in this paper. Specifically, according to a task modularization, the MEC server only delivers the components which have not been stored in the VR device, and then the VR device uses the received components and other cached components to construct the task, yielding low communication cost but high delay. The MEC server also computes the task by itself to reduce the delay, however, it consumes more communication-resource due to the delivery of entire task. Therefore, we propose a task scheduling strategy to decide which computation model should the MEC server operates to minimize the communication-resource consumption under the delay constraint. Finally, the tradeoffs among communications, computing, and caching are also discussed, and we analytically find that given a target communication-resource consumption, the transmission rate is inversely proportional to the computing ability of mobile VR device.

关键词： Mobile edge computing   edge service   service maintenance   skyline   skyline graph  

摘要： The emergence of many new computing applications, such as Internet of Vehicles (IoV) and smart homes, has been made possible by the large pool of cloud resources and services. However, the cloud computing paradigm is unable to meet the requirements of delay-sensitive business applications, such as low latency, mobility support, and location awareness. In this context, Mobile Edge Computing (MEC) is introduced to improve the quality of experience (QoE) by bringing cloud resources and services closer to the user by leveraging available resources in the edge networks. However, the performance of MEC is dynamic in nature due to its location awareness, mobility and proximity. As a result, an effective mechanism is needed for providing efficient dynamic service maintenance for edge services. In this paper, we propose applying the Skyline Graph Model and employing the Directed Acyclic Graph theory to store and update mobile edge services. Specifically, the Skyline Graph (SG) algorithm is designed to solve the insertion, deletion, updating and searching of mobile edge services to achieve efficient maintenance for edge services. Comprehensive experiments are conducted on both real-world web services and simulated datasets to evaluate the effectiveness and efficiency of our approaches. The results show that our algorithms can achieve significantly better performance and robustness than the baseline algorithm.

关键词： Computation offloading   mobile edge computing   resource management   optimization  

摘要： Mobile edge computing (MEC) can augment the computation capabilities of mobile terminals (MTs) through offloading the computational tasks from the MTs to the MEC-enabled base station (MEC-BS) covering them. However, the load of MEC-BS will rise as the increase of the scale of tasks. Existing schemes try to alleviate the load of MEC-BS through refusing, postponing, or queuing the offloading requests of the MTs;thus, the users' QoS will largely deteriorate due to service interruption and prolonged waiting and execution time. In this paper, we investigate the cooperations of multiple MEC-BSs and propose a novel scheme to enhance the computation offloading service of an MEC-BS through further offloading the extra tasks to other MEC-BSs connected to it. An optimization algorithm is proposed to efficiently solve the optimization problem which maximizes the total benefits of time and energy consumptions gained by all the MTs covered by the MEC-BS. A balance factor is used to flexibly adjust the bias of optimization between minimizations of time and energy consumption. Extensive simulations are carried out in eight different scenarios, and the results demonstrate that our scheme can largely enhance the system performance, and it outperforms the reference scheme in all scenarios.

关键词： Mobile edge computing   self-organization   small cell   power optimization   coverage and spectral efficiency  

摘要： Mobile edge computing (MEC) technology migrates the cloud computing platform to the edge of the mobile access network, which reduces the end-to-end delay of mobile service delivery and improves the user experience. However, since the number of the deployed small cell base stations (BSs) is larger than the macro BSs, automatic deployment can be used to control deployment costs. In this paper, we study the adaptive boundary algorithm of indoor small cell BSs and propose a power optimization scheme in self-organizing MEC-based heterogeneous small cell networks. According to the actual environment, the indoor small cell BS can be deployed quickly and flexibly. And the coverage of the small cell BS can be optimized by automatically adjusting the power parameters. Considering the coverage and spectral efficiency of the macro BS and the small cell BS, the performance indicators of the optimization program and results evaluation is proposed. Finally, the simulation results show that the proposed algorithm and scheme can control the deployment cost and improve the optimization coverage of macro BSs and small cell BSs under the control cost.