关键词： Mobile edge computing   service migration   live migration   migration path selection   cellular handover  

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： Forward error correction   Mobile edge computing   Next generation mobile network   Online algorithms  

摘要： Fifth generation of mobile networks (5G) comes to cope with the context of the new mobile telecommunications era. Edge computing is a new collaborative technology under standardization utilizing end-user devices or near-end-user edge devices to operate processing, communication, or control operations. The achievement of efficient error control is a very critical aspect in the successful development of 5G Next Generation Mobile Networks (NGMN). Especially utilizing Forward Error Correction (FEC) codes on the application level is an efficient approach on improving error control on NGMN since it is adopted in several mobile multicast standards. FEC is a feedback free error control technique where redundant data are introduced with the source data to enable the recipient recovering from packet losses with an interesting approach on applying AL-FEC error protection introduces deterministic or randomized online algorithms. Based on this we present a novel online scheme on applying AL-FEC application on the context of the AL-FEC policy online problem. In this work we present an online algorithm based on feedback received from mobile end-users which adapts the introduced protection based on this. Another aspect of this work is the utilization of AL-FEC protection on the edge level based on RaptorQ FEC codes. © Advances in Science, Technology and Engineering Systems. All rights reserved.

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： 5G   ultra-reliable low latency communications   mobile edge computing   computation offloading  

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  

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  

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  

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： Computation offloading   Mobile edge computing   Vehicular ad-hoc networks   Vehicular cloud Computing  

摘要： Smart vehicles of today on road are equipped with advanced computational units, multiple communication technologies, intelligent sensing platforms, and human-computer interaction devices which utilize Vehicular Edge Networks to support services offered by the remote cloud. This being named as Opportunistic Vehicular Edge Computing recently, has the possibility to supplement the services provided by the Edge gadgets. Many Vehicular Edge Computing architectures have been proposed as of late which support task offloading. One among the premier difficulties in these networks is efficiently utilizing the resources available at the vehicular nodes. The present work uses APEATOVC, a conveyed and versatile protocol for economical, efficient and effective task offloading in these networks which address the adaptability of vehicular clouds. The results obtained by extensive simulations are presented to assess and contrast its performance with existing protocols. © 2018, International Journal of Computer Networks & Communications (IJCNC).

关键词： Mobile edge computing  

摘要： —Mobile-edge computing (MEC) is a promising technology to enable real-time information transmission and computing by offloading computation tasks from wireless devices to network edge. In this study, we propose a price-based distributed method to manage the offloaded computation tasks from users. A Stackelberg game is formulated to model the interaction between the edge cloud and users, where the edge cloud sets prices to maximize its revenue subject to its finite computation capacity, and for given prices, each user locally makes offloading decision to minimize its own cost which is defined as latency plus payment. Depending on the edge cloud’s knowledge of the network information, we develop the uniform and differentiated pricing algorithms, which can both be implemented in distributed manners. Simulation results validate the effectiveness of the proposed schemes. Copyright © 2017, The Authors. All rights reserved.

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.