关键词： Mobile edge computing   service migration   live migration   migration path selection   cellular handover   VIRTUAL MACHINE MIGRATION   CLOUD   MANAGEMENT   INTERWORKING   ALLOCATION   PLACEMENT   NETWORKS  

摘要： Mobile edge computing (MEC) provides a promising approach to significantly reduce network operational cost and improve quality of service (QoS) of mobile users by pushing computation resources to the network edges, and enables a scalable Internet of Things (IoT) architecture for time-sensitive applications (e-healthcare, real-time monitoring, and so on.). However, the mobility of mobile users and the limited coverage of edge servers can result in significant network performance degradation, dramatic drop in QoS, and even interruption of ongoing edge services; therefore, it is difficult to ensure service continuity. Service migration has great potential to address the issues, which decides when or where these services are migrated following user mobility and the changes of demand. In this paper, two conceptions similar to service migration, i.e., live migration for data centers and handover in cellular networks, are first discussed. Next, the cutting-edge research efforts on service migration in MEC are reviewed, and a devisal of taxonomy based on various research directions for efficient service migration is presented. Subsequently, a summary of three technologies for hosting services on edge servers, i.e., virtual machine, container, and agent, is provided. At last, open research challenges in service migration are identified and discussed.

关键词： Mobile edge computing  

摘要： With the advances of new-generation wireless and mobile communication systems such as the fifth-generation (5G) mobile networks and Internet of Things (loT) networks, demanding applications such as Ultra-High-Definition video applications is becoming ever popular. These applications require real-time monitoring and processing to meet the mission-critical quality of service requirements and are expected to be supported by the emerging fog or edge computing paradigms. This paper presents NFVMon, a novel monitoring architecture to enable flow monitoring capabilities of network traffic in a 5G multioperator mobile edge computing environment. The proposed NFVMon is integrated with the management plane of the Cloud Computing. NFVMon has been prototyped and a reference implementation is presented. It provides novel capabilities to provide disaggregated metrics related to the different 5G mobile operators sharing infrastructures and also about the different 5G subscribers of each of such mobile operators. Extensive experiments for evaluating the performance of the system have been conducted on a mid-sized infrastructure testbed.

关键词： 3 G Mobile Communication   5 G Mobile Communication   Augmented Reality   Internet   Mobile Computing   Online Front Ends   Weak Computational Efficiency   Browser Cloud Approach   High Latency Dilemma   Mobile Edge Computing   Network Edge   Pervasive AR Application Provisioning   5 G Networks   3 G Networks   4 G Networks   Dedicated Device Based Augmented Reality Solution   App Based Augmented Reality Solution   Web Based AR   Current Web Browsers   Network Latency Reduction   MEC Oriented Web AR Solution   Browsers   Cloud Computing   Augmented Reality   Edge Computing   Servers   Computational Efficiency   5 G Mobile Communication   Internet   Augmented Reality   Web Based AR   Mobile Edge  

摘要： Dedicated device-based and app-based augmented reality (AR) solutions have inherent limitations regarding cross-platform, pervasive AR application provisioning. Web-based AR (web AR), a promising lightweight and cross-platform approach to AR, is gaining increasing attention owing to its extensive application areas. However, for computationally intensive AR applications, the weak computational efficiency of current web browsers seriously hampers applications of web AR on a large scale. The "browser + cloud" approach suffers from the high-latency dilemma. Now, with the emerging 5G networks, mobile edge computing (MEC) promises to greatly reduce network latency (even to 1 ms) by deploying applications at the network edge closer to users, which provides an opportunity for performance improvement of web AR. In this article, the authors envision that the application of MEC has the potential to bring web AR into a new era. Specifically, an MEC-oriented web AR solution is first proposed, followed by the design and deployment details. The authors also discuss future directions aimed at using MEC to tackle the performance issues of web AR in 3G, 4G, and 5G networks.

关键词： 5G   ultra-reliable low latency communications   mobile edge computing   computation offloading   COMPUTATIONAL RESOURCES   OPTIMIZATION   RADIO  

摘要： The ultra-reliable low latency communications (uRLLC) in the fifth generation mobile communication system aims to support diverse emerging applications with strict requirements of latency and reliability. Mobile edge computing (MEC) is considered as a promising solution to reduce the latency of computation-intensive tasks leveraging powerful computing units at short distance. The state-of-art work on task offloading to MEC mainly focuses on the tradeoff between latency and energy consumption, rather than reliability. In this paper, the tradeoff between the latency and reliability in task offloading to MEC is studied. A framework is provided, where user equipment partitions a task into sub-tasks and offloads them to multiple nearby edge nodes (ENs) in sequence. In this framework, we formulate an optimization problem to jointly minimize the latency and offloading failure probability. Since the formulated problem is nonconvex, we design three algorithms based on heuristic search, reformulation linearization technique and semi-definite relaxation, respectively, and solve the problem through optimizing EN candidates selection, offloading ordering and task allocation. Compared with the previous work, the numerical simulation results show that the proposed algorithms strike a good balance between the latency and reliability in uRLLC. Among them, the Heuristic Algorithm achieves the best performance in terms of the latency and reliability with the minimal complexity.

关键词： Mobile cloud computing   mobile edge computing   offloading   decision-making   energy-efficient   PERFORMANCE ANALYSIS   ENERGY   EXECUTION   TRANSMISSION   SMARTPHONES   CHALLENGES   ALGORITHM   SELECTION   MODELS  

摘要： Running very complex applications on mobile devices is still challenging since they are constrained by limited resources, such as memory capacity, network bandwidth, processor speed, and battery power. Mobile Cloud Computing (MCC) is a combination of cloud computing and mobile internet, which could effectively alleviate the resource constraints of mobile devices. How to efficiently offload computation-intensive parts of mobile applications from mobile devices to capable cloud servers is one of the keys. In mobile environments, the resource heterogeneity of mobile devices and cloud services, the interruption of heterogeneous wireless networks, the complexity of mobile applications, and the characteristic of transferring a large amount of data, are the major bottlenecks that have prevented this technology from being widely used. This paper takes these constraints into an account at the same time and explores methods of multi-objective decision making for time- and energy-aware task offloading for MCC. It is designed to ensure the right computational tasks are executed in the right way, at the right time and place.

关键词： Mobile edge computing   deep learning   proactive caching   prediction model searching   POPULARITY PREDICTION   CLOUD  

摘要： Caching popular contents at edge nodes such as base stations is a crucial solution for improving users' quality of services in next-generation networks. However, it is very challenging to correctly predict the future popularity of contents and decide which contents should be stored in the base station cache. Recently, with the advances in big data and high computing power, deep learning models have achieved high prediction accuracy. Hence, in this paper, deep learning is used to learn and predict the future popularity of contents to support cache decision. First, deep learning models are trained and utilized in the cloud data center to make an efficient cache decision. Then, the final cache decision is sent to each base station to store the popular contents proactively. The proposed caching scheme involves three distinct parts: 1) predicting the future class label of each content; 2) predicting the future popularity score of contents based on the predicted class label; and 3) caching the predicted contents with high popularity scores. The prediction models using the Keras and Tensorflow libraries are implemented in this paper. Finally, the performance of the caching schemes is tested with a Python-based simulator. In terms of a cache hit, simulation results show that the proposed scheme outperforms 38%, convolutional recurrent neural network-based scheme outperforms 33%, and convolutional neural network-based scheme outperforms 25% compared to the baseline scheme.

关键词： Blockchain   mobile edge computing   therapy   IoT   COMPUTATION   INTERNET   THINGS   OPTIMIZATION   COMMUNICATION   MANAGEMENT  

摘要： Mobile edge computing (MEC) is being introduced and leveraged in many domains, but few studies have addressed MEC for secure in-home therapy management. To this end, this paper presents an in-home therapy management framework, which leverages the IoT nodes and the blockchain-based decentralized MEC paradigm to support low-latency, secure, anonymous, and always-available spatiotemporal multimedia therapeutic data communication within an on-demand data-sharing scenario. To the best of our knowledge, this non-invasive, MEC-based IoT therapy platform is first done by our group. This platform can provide a full-body joint range of motion data for physically challenged individuals in a decentralized manner. With MEC, the framework can provide therapy diagnostic and analytical data on demand to a large portion of humanity who are either born with disabilities or became disabled due to accidents, war-time injuries, or old age. For security, the framework uses blockchain-Tor-based distributed transactions to preserve the therapeutic data privacy, ownership, generation, storage, and sharing. Our initial test results from a complete implementation of the framework show that it can support a sufficiently large number of users without considerable increase in mean processing time.

关键词： cloud computing   fog computing   edge computing   mobile edge computing   dew computing  

摘要： With the rapid development of pervasive intelligent devices and ubiquitous network technologies, new network applications are emerging, such as the Internet of Things, smart cities, smart grids, virtual/augmented reality, and unmanned vehicles. Cloud computing, which is characterized by centralized computation and storage,is having difficulty meeting the needs of these developing technologies and applications. In recent years, a variety of network computing paradigms, such as fog computing, mobile edge computing, and dew computing, have been proposed by the industrial and academic communities. Although they employ different terminologies, their basic concept is to extend cloud computing and move the computing infrastructure from remote data centers to edge routers, base stations, and local servers located closer to users, thereby overcoming the bottlenecks experienced by cloud computing and providing better performance and user experience. In this paper, we systematically summarize and analyze the post-cloud computing paradigms that have been proposed in recent years. First, we summarize the main bottlenecks of technology and application that cloud computing encounters. Next, we analyze and summarize several post-cloud computing paradigms, including fog computing, mobile edge computing, and dew computing.Then, we discuss the development opportunities of post-cloud computing via several examples. Finally, we note the future development prospects of post-cloud computing.

关键词： cloud computing   fog computing   edge computing   mobile edge computing   resource management and code scheduling  

摘要： Recently, several novel computing paradigms are proposed, e.g., fog computing and edge computing. In such more decentralized computing paradigms, the location and resource for code execution and data storage of end applications could also be optionally distributed among different places or machines. In this paper, we position that this situation requires a new transparent and usercentric approach to unify the resource management and code scheduling from the perspective of end users. We elaborate our vision and propose a software-defined code scheduling framework. The proposed framework allows the code execution or data storage of end applications to be adaptively done at appropriate machines under the help of a performance and capacity monitoring facility, intelligently improving application performance for end users. A pilot system and preliminary results show the advantage of the framework and thus the advocated vision for end users.

关键词： Mobile edge computing   Cooperative cloudlets   Global cloudlet   Power consumption optimization   Delay  

摘要： Reducing the total power consumption and network delay are among the most interesting issues facing large-scale Mobile Cloud Computing (MCC) systems and their ability to satisfy the Service Level Agreement (SLA). Such systems utilize cloud computing infrastructure to support offloading some of user's computationally heavy tasks to the cloud's datacenters. However, the delay incurred by such offloading process lead the use of servers (called cloudlets) placed in the physical proximity of the users, creating what is known as Mobile Edge Computing (MEC). The cloudlet-based infrastructure has its challenges such as the limited capabilities of the cloudlet system (in terms of the ability to serve different request types from users in vast geographical regions). To cover the users demand for different types of services and in vast geographical regions, cloudlets cooperate among each other by passing user requests from one cloudlet to another. This cooperation affects both power consumption and delay. In this work, we present a mixed integer linear programming (MILP) optimization model for MEC systems with these two issues in mind. Specifically, we consider two types of cloudlets: local cloudlets and global cloudlets, which have higher capabilities. A user connects to a local cloudlet and sends all of its traffics to it. If the local cloudlet cannot serve the desired request, then the request is moved to another local cloudlet. If no local cloudlet can serve the request, then it is moved to a global cloudlet which can serve all service types. The process of routing requests through the hierarchical network of cloudlets increases power consumption and delay. Our model minimizes power consumption while incurring an acceptable amount of delay. We evaluate it under several realistic scenarios to show that it can indeed be used for power optimization of large-scale MEC systems without violating delay constraints.