Hybrid Approach for Cost Efficient Application Placement in Fog-Cloud Computing Environments

Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data sources.However,the majority of the fog nodes in this environment are geographically scattered with resources that are limited in terms of capabilities compared to cloud nodes,thus making the application placement problem more complex than that in cloud computing.An approach for cost-efficient application placement in fog-cloud computing environments that combines the benefits of both fog and cloud computing to optimize the placement of applications and services while minimizing costs.This approach is particularly relevant in scenarios where latency,resource constraints,and cost considerations are crucial factors for the deployment of applications.In this study,we propose a hybrid approach that combines a genetic algorithm(GA)with the Flamingo Search Algorithm(FSA)to place application modules while minimizing cost.We consider four cost-types for application deployment:Computation,communication,energy consumption,and violations.The proposed hybrid approach is called GA-FSA and is designed to place the application modules considering the deadline of the application and deploy them appropriately to fog or cloud nodes to curtail the overall cost of the system.An extensive simulation is conducted to assess the performance of the proposed approach compared to other state-of-the-art approaches.The results demonstrate that GA-FSA approach is superior to the other approaches with respect to task guarantee ratio(TGR)and total cost.

Computing Paradigms in Emerging Vehicular Environments:A Review

Determining how to structure vehicular network environments can be done in various ways.Here,we highlight vehicle networks’evolution from vehicular ad-hoc networks(VANET)to the internet of vehicles(Io Vs),listing their benefits and limitations.We also highlight the reasons in adopting wireless technologies,in particular,IEEE 802.11 p and 5 G vehicle-toeverything,as well as the use of paradigms able to store and analyze a vast amount of data to produce intelligence and their applications in vehicular environments.We also correlate the use of each of these paradigms with the desire to meet existing intelligent transportation systems’requirements.The presentation of each paradigm is given from a historical and logical standpoint.In particular,vehicular fog computing improves on the deficiences of vehicular cloud computing,so both are not exclusive from the application point of view.We also emphasize some security issues that are linked to the characteristics of these paradigms and vehicular networks,showing that they complement each other and share problems and limitations.As these networks still have many opportunities to grow in both concept and application,we finally discuss concepts and technologies that we believe are beneficial.Throughout this work,we emphasize the crucial role of these concepts for the well-being of humanity.

A Reverse Auction Mechanism for Time-Varying Multidimensional Resource Allocation in Vehicular Fog Computing with Cloud and Edge Collaboration

It is a hot issue to allocate resources using auction mechanisms in vehicular fog computing(VFC)with cloud and edge collaboration.However,most current research faces the limitation of only considering single type resource allocation,which cannot satisfy the resource requirements of users.In addition,the resource requirements of users are satisfied with a fixed amount of resources during the usage time,which may result in high cost of users and even cause a waste of resources.In fact,the actual resource requirements of users may change with time.Besides,existing allocation algorithms in the VFC of cloud and edge collaboration cannot be directly applied to time-varying multidimensional resource allocation.Therefore,in order to minimize the cost of users,we propose a reverse auction mechanism for the time-varying multidimensional resource allocation problem(TMRAP)in VFC with cloud and edge collaboration based on VFC parking assistance and transform the resource allocation problem into an integer programming(IP)model.And we also design a heuristic resource allocation algorithm to approximate the solution of the model.We apply a dominant-resource-based strategy for resource allocation to improve resource utilization and obtain the lowest cost of users for resource pricing.Furthermore,we prove that the algorithm satisfies individual rationality and truthfulness,and can minimize the cost of users and improve resource utilization through comparison with other similar methods.Above all,we combine VFC smart parking assistance with reverse auction mechanisms to encourage resource providers to offer resources,so that more vehicle users can obtain services at lower prices and relieve traffic pressure.

Fog computing for vehicular Ad-hoc networks: paradigms, scenarios, and issues

Vehicular Ad-hoc networks（VANETs） are kinds of mobile Ad-hoc networks（MANETs）, which consist of mobile vehicles with on-board units（OBUs） and roadside units（RSUs）. With the rapid development of computation and communication technologies, peripheral or incremental changes in VANETs evolve into a revolution in process. Cloud computing as a solution has been deployed to satisfy vehicles in VANETs which are expected to require resources（such as computing, storage and networking）. Recently, with special requirements of mobility, location awareness, and low latency, there has been growing interest in research into the role of fog computing in VANETs. The merging of fog computing with VANETs opens an area of possibilities for applications and services on the edge of the cloud computing. Fog computing deploys highly virtualized computing and communication facilities at the proximity of mobile vehicles in VANET. Mobile vehicles in VANET can also demand services of low-latency and short-distance local connections via fog computing. This paper presents the current state of the research and future perspectives of fog computing in VANETs. Moreover, we discuss the characteristics of fog computing and services based on fog computing platform provided for VANETs. In this paper, some opportunities for challenges and issues are mentioned, related techniques that need to be considered have been discussed in the context of fog computing in VANETs. Finally, we discuss about research directions of potential future work for fog computing in VANETs. Within this article, readers can have a more thorough understanding of fog computing for VANETs and the trends in this domain.

Edge Computing Based Applications in Vehicular Environments:Comparative Study and Main Issues

Despite the expanded efforts,the vehicular ad-hoc networks(VANETs)are still facing many challenges such as network performances,network scalability and context-awareness.Many solutions have been proposed to overcome these obstacles,and the edge computing,an extension of the cloud computing,is one of them.With edge computing,communication,storage and computational capabilities are brought closer to end users.This could offer many benefits to the global vehicular network including,for example,lower latency,network off-loading and context-awareness(location,environment factors,etc.).Different approaches of edge computing have been developed:mobile edge computing(MEC),fog computing(FC)and cloudlet are the main ones.After introducing the vehicular environment background,this paper aims to study and compare these different technologies.For that purpose their main features are compared and the state-of-the-art applications in VANETs are analyzed.In addition,MEC,FC,and cloudlet are classified and their suitability level is debated for different types of vehicular applications.Finally,some challenges and future research directions in the fields of edge computing and VANETs are discussed.

基于移动云服务的车联网数据上传策略

传统的基于专用短程通信(dedicated short range communication,DSRC)的车载网络(vehicular ad hoc network,VANET)通信架构难以满足车联网数据传输的服务质量(quality of service,QoS)需求,通过移动网关将数据上传至服务器,由服务器决策传输给目标车辆,可以扩大数据广播域,极大减少数据远程传输时延.结合移动云服务的思想,提出了一种新的车联网架构和数据传输方法.首先给出了网关服务者(gateway server,GWS)向云端注册服务信息的具体流程;其次提出了一种云端服务网关选取方法,该方法结合云端的历史数据和实时数据,动态决定参与服务的网关服务者及其服务范围,网关消费者(gateway consumer,GWC)在获取服务广播消息后,综合考虑通信负载、链路稳定度、信道质量等性能参数来选出最优的网关服务者,并将数据传输给网关服务者,再由其上传到云端;最后在OMNeT++实验环境下,针对不同的交通场景,对该方法传输性能进行了评估.结果表明该方法获得较低传输延迟的同时,能够保证较高的传输成功率,理论分析也证明了该方法的有效性.

大数据的后时代:雾计算的教育应用探析

当前大数据处理技术已进入后时代,以分布式计算为特征的雾技术正在IT云技术生态系统中引发革命性变革。然而,将雾技术引入教育领域的专门研究仍十分缺乏。因此,文章从雾技术的核心内涵以及特征入手,重点探讨雾技术的教育应用价值及典型案例,并针对雾技术的应用瓶颈,提出建立教育应用整体框架、实现雾平台可视化服务、强化雾资源实时管理、加强雾隐私安全保障以及优化雾计算技术标准等未来发展策略。

移动环境下基于物联网层、雾层及云层的医疗健康服务体系研究

阐述医疗健康服务体系物联网端、雾端、云端相关研究现状,提出具有物联网层、雾层、云层的医疗健康服务体系架构,详细介绍每层使用的组件,总结该体系在医疗健康行业应用场景和功能。

基于车联网的智能交通系统研究

面向未来智能交通系统,文章分析了该系统网络的固有属性。围绕这些属性构建了车联网智能交通系统的架构,在此基础上分析了基于"云-雾-节点"模式的信息交互模型,提出目前存在的关键问题,以期为车联网智能交通系统研究提供参考。

基于雾计算的高速公路服务区内容访问技术研究

车联网(Vehicular Ad hoc Networks,VANETs)在高速公路上具有车辆高速行驶、密度低、通信基础设施稀缺、车辆连通性低等特点,使得高速公路上的车辆难以实现对其他车辆或路边单元(Road Side Unit,RSU)的内容访问。提出了一种在高速公路服务区利用雾计算以协助车辆获取感兴趣内容的模型。该车辆雾计算(Vehicle Fog Comput-ing, VFC)模型中,高速公路服务区收集来自各个地方的车辆提供的各种服务,将大量的停泊车辆和慢速行驶车辆作为雾设备。通过VFC本地化转发,不仅减少了通信延迟,还实现了令人满意的内容访问和实时数据流传输。此外,对通信能量消耗与系统延迟之间的关系进行公式化,并在雾计算中采用了外部近似(Outer Approximation,OA)算法来优化其权衡。仿真结果表明,通过采用雾计算和云计算结合的通信模式和均衡优化算法,随着能量消耗的增长,系统的通信延迟会明显地降低。

基于可信计算的车联网云安全模型

基于可信计算的思想,提出基于可信计算的车联网云计算安全模型的架构;针对车载通信设备提出了身份认证和信任度评估的框架,并在该框架基础上给出了密钥管理结构和移动节点认证过程;在车联网云平台的各个虚拟机上构建起基于TPCM(trusted platform control module)的信任链,以此为基础为车联网云提供一个安全的运行环境.

车载自组织网络通信协议

车载自组织网络(VANET)被认为是未来智能交通系统(ITS)的重要组成部分,可有效提高行车效率、安全性和舒适性。VANET属于移动自组织网络(MANET),不依赖固定设施,其节点高速移动、网络拓扑变化频繁,具有与MANET不同的构架、特征、应用和挑战。本文综述了VANET网络构架、通信协议、建模与仿真、信息管理和云计算等方面的研究进展,展望了未来的发展方向。

基于离散优化算法和机器学习的传感云入侵检测

针对传感云的大规模高维度数据和多变性入侵行为,在雾计算模式下提出了一种基于并行离散优化特征提取和机器学习方法特性的传感云入侵检测算法。首先,为有效降低数据维度和提高特征提取过程的鲁棒性,在定义最优特征评价指标的基础上构建并行离散优化特征提取框架,理论分析表明:该指标能最大限度去除特征冗余度和保持原始数据多样性。其次,设计了具有普遍意义的离散优化算法(DOA),结合工程优化问题特点给出DOA实现流程,并证明了DOA具有全局收敛性,在此基础上使用DOA对特征提取框架进行求解,通过并行特征子集筛选过程实现了最佳特征组合提取。最后,利用最佳特征子集和机器学习中的分布模糊聚类技术对传感云入侵行为进行检测,通过引入智能迭代进化思想和自适应聚类策略,在有效避免模糊聚类算法易陷入局部最优缺陷的同时实现了聚类个数自动划分。实验结果表明:该入侵检测算法能有效给出入侵检测结果,而且相比于其他检测算法,该算法异常检测成功率和漏检率明显改善,且具有较强抗噪能力。