关键词： Mobile edge computing   fog computing   mobile cloud computing   computation offloading   resource management   green computing  

摘要： Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized mobile cloud computing toward mobile edge computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also discuss a set of issues, challenges, and future research directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

关键词： Task scheduling   mobile edge computing   overhead analysis   potential game   optimal strategy  

摘要： In this paper, an overhead-optimizing multi-device task scheduling strategy for ad-hoc-based mobile edge computing system is proposed. This task scheduling strategy takes the opportunity consumption, time delay, energy consumption, and monetary cost into account, aiming at minimizing the overhead of each mobile device. First, a system model for ad-hoc-based mobile edge computing is presented and the overhead of mobile device is analyzed. Second, the task scheduling problem is formulated as a distributed multi-device task scheduling game. Then, by constructing a potential function, the task scheduling game is proved to be a potential game, which possesses a property of finite improvement and always owns a Nash equilibrium. Next, an overhead-optimizing multi-device task scheduling algorithm is designed and the computational complexity is analyzed. Finally, simulations are conducted to evaluate the effectiveness of the proposed strategy. The results show that the proposed task scheduling strategy can effectively minimize the overhead of the mobile device and successfully complete the tasks.

关键词： Mobile edge computing   computation offloading   MEC SP and UE matching   combinational auction   demand heterogeneity  

摘要： Via processing the computation intensive applications (apps) at the network edge, mobile edge computing (MEC) becomes a promising technology to enhance the ability of the user equipments (UEs). Most existing works usually focus on whether to offload or where to offload the apps under the premise that sufficient resources are owned by the network edge. However, the demand heterogeneity of UEs and the limitation of resources are usually failed to be considered. Since the limited resources may constrain the number of accessed UEs, how the MEC service providers (SPs) choose the UEs to serve while ensuring UEs' Quality of Service (QoS) is a key issue. Under this context, in this paper, we study the matching problem between the MEC SPs and the UEs in a multi-MEC and multi-UE scenario. Within this scenario, MEC SPs are equipped with limited wireless and computational resources. Auction theory is utilized to model the matching relationship between MEC SPs and UEs as the commodity trading. With this trading, UEs can obtain MEC service from SPs, when they successfully purchase the combinational resources (including computational and wireless resources) from SPs. To complete the auction process, a multi-round-sealed sequential combinatorial auction mechanism is proposed. The properties of the auction are proved and various simulation results are done to show that the proposed approach has better system performance compared with the existing algorithms.

关键词： Mobile edge computing   computation offloading   energy minimization   branch-and-bound method   reformulation-linearization-technique   Gini coefficient  

摘要： Mobile edge computing (MEC) providing information technology and cloud-computing capabilities within the radio access network is an emerging technique in fifth-generation networks. MEC can extend the computational capacity of smart mobile devices (SMDs) and economize SMDs' energy consumption by migrating the computation-intensive task to the MEC server. In this paper, we consider a multi-mobile-users MEC system, where multiple SMDs ask for computation offloading to a MEC server. In order to minimize the energy consumption on SMDs, we jointly optimize the offloading selection, radio resource allocation, and computational resource allocation coordinately. We formulate the energy consumption minimization problem as a mixed interger nonlinear programming (MINLP) problem, which is subject to specific application latency constraints. In order to solve the problem, we propose a reformulation linearization-technique-based Branch-and-Bound (RLTBB) method, which can obtain the optimal result or a suboptimal result by setting the solving accuracy. Considering the complexity of RTLBB cannot be guaranteed, we further design a Gini coefficient-based greedy heuristic (GCGH) to solve the MINLP problem in polynomial complexity by degrading the MINLP problem into the convex problem. Many simulation results demonstrate the energy saving enhancements of RLTBB and GCGH.

关键词： Mobile edge computing   Resource-hungry task   Two-dimensional knapsack problem  

摘要： Cloud-enabled vehicular network is a promising paradigm to meet the ever-increasing demands from various mobile applications. However, huge transmission delay and serious degradation may occur currently due to the long distance away from vehicular terminals. Thus, a new issue named Mobile Edge Computing (MEC) is established as an effective way to provide cloud computing capabilities at the edge of pervasive radio access networks, in close proximity to vehicular terminals. In this paper, we build a MEC based vehicular network framework to solve offloading and resource allocation problem of resource-hungry tasks such as data and computing dual intensive task by collaborative computing in the multi-user single-task environment, in the meantime, reducing latency and improving the resource utilization efficiently. First, we come up with an appropriate task offloading decision for every vehicular terminal to decide the proportion between local computing and mobile edge computing in the form of a delay-constraint objective function by considering both moving pattern of vehicles and channel conditions. Then, a joint resource allocation scheme is designed through solving a two-dimensional knapsack problem. Efficient resource allocation results can be obtained by maximizing the sum of time duration difference between local computing only and collaborative computing. Simulation results are provided to validate the effectiveness of proposed work and demonstrate the impacts of various parameters to the system performance.

关键词： Mobility   mobile edge computing   coded caching   probabilistic caching   throughput   stochastic geometry   trade-off  

摘要： Caching on the edge has been recognized as an effective solution to tackle the backhaul constraint of network densification. However, most related works ignored user mobility in wireless networks, which is unreasonable under the background of network densification. For a more flexible and context-aware caching decision, the concept of caching on the edge can be extended to mobile edge computing (MEC) that enables computation and storage resources at mobile edge networks. With MEC servers deployed on base stations, a huge amount of collected radio access network context data can be analyzed and utilized to render a caching scheme adaptive to user's context-aware information. In this regard, a novel mobility-aware coded probabilistic caching scheme is proposed for MEC-enabled small cell networks (SCNs). Different from previous mobility-aware caching schemes, user mobility and distributed storage are incorporated into a conventional probabilistic caching scheme, with the aim of throughput maximization. Based on stochastic geometry theory and a modified mobility model of discrete random jumps, the explicit expression of throughput is derived. Due to the complexity of the expression, two light-weight heuristic algorithms are provided to numerically obtain the optimal solutions. Moreover, a significant trade-off among the gains of mobility diversity, content diversity, and channel selection diversity is discussed, and we further numerically analyze how such a trade-off is influenced by user mobility, content popularity, and backhaul capacity, with some fundamental insights into the application of the proposed scheme in MEC-enabled SCNs. The superiority of our proposed scheme is demonstrated by the comparisons with the classical M most popular caching scheme and the conventional probabilistic caching scheme. Numerical results show that our proposed caching scheme achieves higher throughput than those of the other two, especially when users of intense mobility request conte

关键词： Mobile edge computing   mobile network architecture   computation offloading   allocation of computing resources   mobility management   standardization   use-cases  

摘要： Technological evolution of mobile user equipment (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. A suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud. Nevertheless, this option introduces significant execution delay consisting of delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such a delay is inconvenient and makes the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling it to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: 1) decision on computation offloading;2) allocation of computing resource within the MEC;and 3) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.

关键词： Fog computing   mobile edge computing   cloudlets   crowd sourcing   crowd sensing  

摘要： The cloud computing paradigm faces the challenges of providing low latency, high availability, and real-time location-aware services where millions of people are mobile with respect to time and geographic location. In this paper, we propose a mobile edge computing framework that can support real-time, location-aware personalized services to a very large crowd. The framework uses a hybrid of cloud at the server end and fog computing terminals (FCTs) at the crowd edge. The concept of FCT is realized by adding a middle layer acting as a proxy between the user end and cloud infrastructure. Each FCT node covers a geographic zone and provides a subset of services and resources based on the geographic location of a mobile user. When a user moves from one FCT-covered zone to another, the secure handshaking of metadata about the user is shared with the new FCT node. The communication between mobile users' terminals, such as smartphone and the FCT, is assumed as 4G/5G networks, while the communication between the FCT and cloud is based on a high speed, always available, and reliable Internet connection. The location of each mobile user is made secure and shared according to our novel privacy policy paradigm. The framework is designed to switch between FCT and cloud, depending on the task, network condition, geographic nearness, and resources available within the client unit. We have implemented the framework to support context-aware services to millions of pilgrims that gather together in a very small area of land each year. We will share the inspiring results that we have gathered after initial deployment.

关键词： Mobile edge computing   mobile edge caching   D2D   SDN   NFV   content delivery   computational offloading  

摘要： As the explosive growth of smart devices and the advent of many new applications, traffic volume has been growing exponentially. The traditional centralized network architecture cannot accommodate such user demands due to heavy burden on the backhaul links and long latency. Therefore, new architectures, which bring network functions and contents to the network edge, are proposed, i.e., mobile edge computing and caching. Mobile edge networks provide cloud computing and caching capabilities at the edge of cellular networks. In this survey, we make an exhaustive review on the state-of-the-art research efforts on mobile edge networks. We first give an overview of mobile edge networks, including definition, architecture, and advantages. Next, a comprehensive survey of issues on computing, caching, and communication techniques at the network edge is presented. The applications and use cases of mobile edge networks are discussed. Subsequently, the key enablers of mobile edge networks, such as cloud technology, SDN/NFV, and smart devices are discussed. Finally, open research challenges and future directions are presented as well.

关键词： mobile edge computing   5G   mobile internet   mobile network   mobile application  

摘要： Mobile Edge Computing(MEC) is an emerging technology in 5G era which enables the provision of the cloud and IT services within the close proximity of mobile *** allows the availability of the cloud servers inside or adjacent to the base *** endto-end latency perceived by the mobile user is therefore reduced with the MEC *** context-aware services are able to be served by the application developers by leveraging the real time radio access network information from *** MEC additionally enables the compute intensive applications execution in the resource constraint devices with the collaborative computing involving the cloud *** paper presents the architectural description of the MEC platform as well as the key functionalities enabling the above *** relevant state-of-the-art research efforts are then *** paper finally discusses and identifies the open research challenges of MEC.