关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) is an emergent technology that has revolutionized traditional cloud service solutions. Mobile edge computing extends cloud computing by providing processing, storage, and networking capabilities at the edge of the mobile network. Delay-sensitive and context-aware applications are able to execute within close proximity of mobile users. Additionally, today's cloud services are not tailored to user specifications, but rather diversified toward a group of users. To guarantee delivery of user-specific services in 5G networks, service composition techniques should be incorporated. This article envisions a real-time, context-aware, service-composition collaborative framework that lies at the edge of the network, comprising MEC and user devices for fast composite service delivery. The proposed solution decomposes cloud data into a set of files and services, which are then replicated to MEC nodes. Frequently requested files and services are further cached onto user mobile devices for faster access. Both MEC nodes and mobile users advertise their services onto the collaborative edge/user space, where services are delivered either composite or unrendered according to users' requests. Service composition is achieved through a learning-based workflow-net approach that relies on previous composition results to build service composition models to be used for new compositions. The presented solution provides guaranteed and fast delivery of the requested cloud composite services to end users while sustaining QoS requirements and load balancing among edge and mobile nodes.

关键词： Computation Offloading   Data Caching   Mobile Collaborative Applications   Mobile Edge Computing   Coalitional Game  

摘要： Computation offloading is a proven successful paradigm for enabling resource-intensive applications on mobile devices. Moreover, in view of emerging mobile collaborative application, the offloaded tasks can be duplicated when multiple users are in the same proximity. This motivates us to design a collaborative offloading scheme and cache the popular computation results that are likely to be reused by other mobile users. In this paper, we consider the scenario where multiple mobile users offload duplicated computation tasks to the network edge, and share the computation results among them. Our goal is to develop the optimal finegrained collaborative offloading strategies with caching enhancements to minimize the overall execution delay at the mobile terminal side. To this end, we propose an optimal offloading with cachingenhancement scheme (OOCS) for femto-cloud scenario and mobile edge computing scenario, respectively. Simulation results show that compared to six alternative solutions in literature, our single-user OOCS can reduce execution delay up to 42.83% and 33.28% for single-user femto-cloud and single-user mobile edge computing, respectively. Our multi-user OOCS can further reduce 11.71% delay compared to single-user OOCS through users' cooperation.

关键词： 5G networks   mobile edge computing   dynamic replication   mobile computing  

摘要： Mobile edge computing (MEC) has been proposed to complement the limitations of cloud computing. Allocating replicas in MEC servers can shorten transmission latency and improve the quality of service (QoS). However, cloud-based replication schemes may not be suitable for MEC servers due to their limited resources and the varied traffic in RANs. In this paper, we analyze the revenue, cost, and profit for the replicas in MEC servers. Accordingly, we present algorithms of adaptive replication for MEC. Our algorithms dynamically allocate replicas based on the number of read/write operations in each time interval. We also employ an algorithm that adaptively forwards requests to replicas in the neighboring MEC servers to balance the load of MEC servers. The simulation results show that our scheme can effectively shorten the average response time and improve the QoS of packets for better profit.

关键词： cost-effective   Internet of Vehicles   volunteer computing   mobile edge computing   vehicular network  

摘要： By bringing the computation and storage resources close proximity to the mobile network edge, mobile edge computing (MEC) is a key enabling technology for satisfying the Internet of Vehicles (IoV) infotainment applications' requirements, e.g., video streaming service (VSA). However, the explosive growth of mobile video traffic brings challenges for video streaming providers (VSPs). One known issue is that a huge traffic burden on the vehicular network leads to increasing VSP costs for providing VSA to mobile users (i.e., autonomous vehicles). To address this issue, an efficient resource sharing scheme between underutilized vehicular resources is a promising solution to reduce the cost of serving VSA in the vehicular network. Therefore, we propose a new VSA model based on the lower cost of obtaining data from vehicles and then minimize the VSP's cost. By using existing data resources from nearby vehicles, our proposal can reduce the cost of providing video service to mobile users. Specifically, we formulate our problem as mixed integer nonlinear programming (MINP) in order to calculate the total payment of the VSP. In addition, we introduce an incentive mechanism to encourage users to rent its resources. Our solution represents a strategy to optimize the VSP serving cost under the quality of service (QoS) requirements. Simulation results demonstrate that our proposed mechanism is possible to achieve up to 21% and 11% cost-savings in terms of the request arrival rate and vehicle speed, in comparison with other existing schemes, respectively.

关键词： Mobile edge computing  

摘要： Mobile-edge computing (MEC) has recently emerged as a cost-effective paradigm to enhance the computing capability of hardware-constrained wireless devices (WDs). In this paper, we first consider a two-user MEC network, where each WD has a sequence of tasks to execute. In particular, we consider task dependency between the two WDs, where the input of a task at one WD requires the final task output at the other WD. Under the considered task-dependency model, we study the optimal task offloading policy and resource allocation (e.g., on offloading transmit power and local CPU frequencies) that minimize the weighted sum of the WDs' energy consumption and task execution time. The problem is challenging due to the combinatorial nature of the offloading decisions among all tasks and the strong coupling with resource allocation. To tackle this problem, we first assume that the offloading decisions are given and derive the closed-form expressions of the optimal offloading transmit power and local CPU frequencies. Then, an efficient bi-section search method is proposed to obtain the optimal solutions. Furthermore, we prove that the optimal offloading decisions follow an one-climb policy, based on which a reduced-complexity Gibbs Sampling algorithm is proposed to obtain the optimal offloading decisions. We then extend the investigation to a general multi-user scenario, where the input of a task at one WD requires the final task outputs from multiple other WDs. Numerical results show that the proposed method can significantly outperform the other representative benchmarks and efficiently achieve low complexity with respect to the call graph size. Copyright © 2018, The Authors. All rights reserved.

关键词： Vehicle-to-Everything communications   Social internet of things   5G   Mobile edge computing   Software-defined networking  

摘要： Vehicle-to-Everything (V2X) communications are widely recognized as a key technology enabler of increased road safety, improved traffic efficiency, and more comfortable traveling experience. Notably, vehicles on the road may share common interests and some mobility patterns determined by the road topology and the human habits and they can cooperate by exchanging valuable information. These social aspects of vehicular mobility can be exploited in many ways, for entertainment, public utility and emergency purposes. In this paper we speculate on how the social dimension can be leveraged in future fifth generation (5G) V2X systems. Specifically, we propose a novel social-enhanced 5G-V2X framework which leverages mobile edge computing (MEC) and software-defined networking (SDN) technologies with the aim to exploit social relationships between V2X entities and facilitate data delivery in this context. Early results are provided to showcase the viability of the proposal in a representative scenario, i.e., safety alert dissemination, and demonstrate its benefits compared to a legacy cellular-aided data dissemination approach. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Mobile edge computing  

摘要： In order to mitigate the long processing delay andhigh energy consumption of mobile augmented reality (AR) applications, mobile edge computing (MEC) has been recently proposed and is envisioned as a promising means to deliver better quality of experience (QoE) for AR consumers. In this article, we first present a comprehensive AR overview, including the indispensable components of general AR applications, fashionable AR devices, and several existing techniques for overcoming the thorny latency and energy consumption problems. Then, we propose a novel hierarchical computation architecture by inserting an edge layer between the conventional user layer and cloud layer. Based on the proposed architecture, we further develop an innovated operation mechanism to improve the performance of mobile AR applications. Three key technologies are also discussed to further assist the proposed AR architecture. Simulation results are finally provided to verify that our proposals can significantly improve the latency and energy performance as compared against existing baseline schemes. Copyright © 2018, The Authors. All rights reserved.

关键词： 5G   Internet of Things   Mobile communication systems   Mobile edge computing  

摘要： The future mobile communication network will face the challenges of high mobile data traffic and more terminal data types, and the resulting effective technology with low latency and low energy consumption is required. Mobile edge computing (MEC) arises as an emergent technology for meeting the above requirements. MEC can run the computing offloading and resources storage on the edge of the network, which can greatly reduce the processing time delay and energy consumption, and contribute to a better quality of user experience. Firstly, the paper summarizes the fundamental principles and technical characteristics of MEC. Then, through specific MEC application scenarios, the paper further summarizes the advantages of MEC. Finally, on the basis of the existing work, the challenges and prospects of MEC are discussed. MEC will promote the integration of computing and communications, and power 5G towards new stage with the multiple technology fusion. © 2018, Editorial Department of Journal of NUAA. All right reserved.

关键词： Auction   mobile edge computing   network economics   resource allocation  

摘要： Mobile edge computing (MEC) yields significant paradigm shift in industrial Internet of things (IIoT), by bringing resource-rich data center near to the lightweight IIoT mobile devices (MDs). In MEC, resource allocation and network economics need to be jointly addressed to maximize system efficiency and incentivize price-driven agents, whereas this joint problem is under the locality constraints, i.e., an edge server can only serve multiple IIoT MDs in the vicinity constrained by its limited computing resource. In this paper, we investigate the joint problem of network economics and resource allocation in MEC where IIoT MDs request offloading with claimed bids and edge servers provide their limited computing service with ask prices. Particularly, we propose two double auction schemes with dynamic pricing in MEC, namely a breakeven-based double auction (BDA) and a more efficient dynamic pricing based double auction (DPDA), to determine the matched pairs between IIoT MDs and edge servers, as well as the pricing mechanisms for high system efficiency, under the locality constraints. Through theoretical analysis, both algorithms are proved to be budget-balanced, individual profit, system efficient, and truthful. Extensive simulations have been conducted to evaluate the performance of the proposed algorithms and the simulation results indicate that the proposed DPDA and BDA can significantly improve the system efficiency of MEC in IIoT.

关键词： multi-access   mobile edge computing   computational power allocation   optimization   Blockchain  

摘要： Blockchain has emerged as a decentralized and trustable ledger for recording and storing digital transactions. The mining process of Blockchain, however, incurs a heavy computational workload for miners to solve the proof-of-work puzzle (i.e., a series of the hashing computation), which is prohibitive from the perspective of the mobile terminals (MTs). The advanced multi-access mobile edge computing (MEC), which enables the MTs to offload part of the computational workloads (for solving the proof-of-work) to the nearby edge-servers (ESs), provides a promising approach to address this issue. By offloading the computational workloads via multi-access MEC, the MTs can effectively increase their successful probabilities when participating in the mining game and gain the consequent reward (i.e., winning the bitcoin). However, as a compensation to the ESs which provide the computational resources to the MTs, the MTs need to pay the ESs for the corresponding resource-acquisition costs. Thus, to investigate the trade-off between obtaining the computational resources from the ESs (for solving the proof-of-work) and paying for the consequent cost, we formulate an optimization problem in which the MTs determine their acquired computational resources from different ESs, with the objective of maximizing the MTs' social net-reward in the mining process while keeping the fairness among the MTs. In spite of the non-convexity of the formulated problem, we exploit its layered structure and propose efficient distributed algorithms for the MTs to individually determine their optimal computational resources acquired from different ESs. Numerical results are provided to validate the effectiveness of our proposed algorithms and the performance of our proposed multi-access MEC for Blockchain.