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   resource allocation   energy efficient   ADMM  

摘要： Mobile edge computing (MEC) is a new promising technique to provide cloud-computing capabilities at the edge of cellular networks in close proximity to mobile users. In this paper, we consider joint optimization of the communication and computation resources in a multiuser, multiserver MEC system. The objective of this optimization problem is to minimize the total energy consumption of mobile devices under the time-sharing constraint. Given the fact that no coordination is involved between mobile devices, we propose a light-weight and decentralized algorithm based on the alternating direction method of multipliers (ADMM) framework. Experimental results demonstrate that the proposed algorithm performs well in terms of convergence and outperforms the conventional centralized approach.

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： 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： Caching   computation offloading   mobile edge computing   energy efficient  

摘要： While augment reality applications are becoming popular, more and more data-hungry and computation-intensive tasks are delay-sensitive. Mobile edge computing is expected to an effective solution to meet the low latency demand. In contrast to previous work on mobile edge computing, which mainly focus on computation offloading, this paper introduces a new concept of task caching. Task caching refers to the caching of completed task application and their related data in edge cloud. Then, we investigate the problem of joint optimization of task caching and offloading on edge cloud with the computing and storage resource constraint. We formulate this problem as mixed integer programming which is hard to solve. To solve the problem, we propose efficient algorithm, called task caching and offloading (TCO), based on alternating iterative algorithm. Finally, the simulation experimental results show that our proposed TCO algorithm outperforms others in terms of less energy cost.

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   resource allocation   fairness   system throughput maximization   minimum rate requirement  

摘要： Communication resource allocation is important for improving the performance of users in mobile edge computing (MEC) scenarios. In existing studies, the users in the MEC system typically suffer from unfair resource allocation, which results in the inefficient resource utilization and degraded user performance. To address this challenge, in this paper we propose a fair resource allocation approach to maximize the overall network throughput, under the constraint of each mobile user's minimum transmission rate. We formulate the problem as a fair Nash bargaining resource allocation game, and the existence and uniqueness of the solution to this game model are analyzed. By adopting the time-sharing variable, we obtain the near optimal bargaining resource allocation strategy for the mixed integer nonlinear programming optimization. The user's priority is further considered in the iterative implementation of the proposed algorithm by considering the time delay constraint of users. Simulation results show that the proposed scheme outperforms existing methods in terms of resource allocation fairness and overall system throughput.

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： 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   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.