Deadline-aware multi-objective IoT services placement optimization in fog environment using parallel FFD-genetic algorithm

Nowadays, Internet of things has become as an inevitable aspect of humans’ IT-based life. A huge number of geo-distributed IoT enabled devices such as smart phones, smart cameras, health care systems, vehicles, etc. are connected to the Internet and manage users’ applications. The IoT applications are generally time sensitive, so that giving them up to Cloud and receiving the response may violate their required deadline, due to distance between user device and centralized Cloud data center and consequently increasing network latency. Fog environment, as an intermediate layer between Cloud and IoT devices, brings a smaller scales of Cloud capabilities closer to user location. Processing real time applications in Fog layer helps more deadlines to be met. Although Fog computing enhances quality of service parameters, limited resources and power of Fog nodes is a challenge in processing applications. Furthermore, the network latency is still an issue for communications between applications’ services and between user device and Fog node, which seriously threatens deadline condition. Regarding to mentioned points, this paper proposes a 3-partite deadline-aware applications’ services placement optimization model in Fog environment which optimizes total power consumption, total resources wastage, and total network latency, simultaneously. The proposed model prioritizes applications in 3 levels based on their associated deadline, and then the model is solved using a parallel model of first fit decreasing and genetic algorithm combination. Simulations results indicates the superiority of proposed approach against counterpart algorithms in terms of reducing power consumption, resource wastage, network latency, and service rejection rate.     

Hierarchical fuzzy-based Quality of Experience (QoE)-aware application placement in fog nodes

Fog computing or a fog network is a decentralized network placed in between data source and the cloud to minimize the network latency issues and thus support in-time service delivery, of Internet of Things (IoT) applications. However, placing computational tasks of IoT applications in fog infrastructure is a challenging task. State of the art focuses on quality of service and quality of experience (QoE) based application placement. In this article, we design hierarchical fuzzy based QoE-aware application placement strategy for mapping IoT applications with compatible instances in the fog network. The proposed method considers user application expectation parameters and metrics of available fog instances, and assigns the priority of applications using hierarchical fuzzy logic. The method later uses Hungarian maximization assignment algorithm to map applications with compatible instances. The simulation results of the proposed policy show better performance over the existing baseline algorithms in terms of resource gain (RG), processing time reduction ratio (PTRR), and similarly network relaxation ratio. When considering 10 applications in the fog network, our proposed method simulation results show 70.00%, 22.44%, 37.83% improvement in RG, and 28.46%, 37.5%, 23.07% improvement in PTRR, when compared with QoE-aware, randomized, FIFO algorithms, respectively.     

Hierarchical Data Aggregation with Data Offloading Scheme for Fog Enabled IoT Environment

Fog computing is a promising technology that has been emerged to handle the growth of smart devices as well as the popularity of latency-sensitive and location-awareness Internet of Things (IoT) services. After the emergence of IoT-based services, the industry of internet-based devices has grown. The number of these devices has raised from millions to billions, and it is expected to increase further in the near future. Thus, additional challenges will be added to the traditional centralized cloud-based architecture as it will not be able to handle that growth and to support all connected devices in real-time without affecting the user experience. Conventional data aggregation models for Fog enabled IoT environments possess high computational complexity and communication cost. Therefore, in order to resolve the issues and improve the lifetime of the network, this study develops an effective hierarchical data aggregation with chaotic barnacles mating optimizer (HDAG-CBMO) technique. The HDAG-CBMO technique derives a fitness function from many relational matrices, like residual energy, average distance to neighbors, and centroid degree of target area. Besides, a chaotic theory based population initialization technique is derived for the optimal initial position of barnacles. Moreover, a learning based data offloading method has been developed for reducing the response time to IoT user requests. A wide range of simulation analyses demonstrated that the HDAG-CBMO technique has resulted in balanced energy utilization and prolonged lifetime of the Fog assisted IoT networks.     

Content-centric data and computation offloading in AI-supported fog networks for next generation IoT

Fog Computing (FC) based IoT applications are encountering a bottleneck in the data management and resource optimization due to the dynamic IoT topologies, resource-limited devices, resource diversity, mismatching service quality, and complicated service offering environments. Existing problems and emerging demands of FC based IoT applications are hard to be met by traditional IP-based Internet model. Therefore, in this paper, we focus on the Content-Centric Network (CCN) model to provide more efficient, flexible, and reliable data and resource management for fog-based IoT systems. We first propose a Deep Reinforcement Learning (DRL) algorithm that jointly considers the content type and status of fog servers for content-centric data and computation offloading. Then, we introduce a novel virtual layer called FogOrch that orchestrates the management and performance requirements of fog layer resources in an efficient manner via the proposed DRL agent. To show the feasibility of FogOrch, we develop a content-centric data offloading scheme (DRLOS) based on the DRL algorithm running on FogOrch. Through extensive simulations, we evaluate the performance of DRLOS in terms of total reward, computational workload, computation cost, and delay. The results show that the proposed DRLOS is superior to existing benchmark offloading schemes.     

Mobile Fog Computing by Using SDN/NFV on 5G Edge Nodes

Fog computing provides quality of service for cloud infrastructure. As the data computation intensifies, edge computing becomes difficult. Therefore, mobile fog computing is used for reducing traffic and the time for data computation in the network. In previous studies, software-defined networking (SDN) and network functions virtualization (NFV) were used separately in edge computing. Current industrial and academic research is tackling to integrate SDN and NFV in different environments to address the challenges in performance, reliability, and scalability. SDN/NFV is still in development. The traditional Internet of things (IoT) data analysis system is only based on a linear and time-variant system that needs an IoT data system with a high-precision model. This paper proposes a combined architecture of SDN and NFV on an edge node server for IoT devices to reduce the computational complexity in cloud-based fog computing. SDN provides a generalization structure of the forwarding plane, which is separated from the control plane. Meanwhile, NFV concentrates on virtualization by combining the forwarding model with virtual network functions (VNFs) as a single or chain of VNFs, which leads to interoperability and consistency. The orchestrator layer in the proposed software-defined NFV is responsible for handling real-time tasks by using an edge node server through the SDN controller via four actions: task creation, modification, operation, and completion. Our proposed architecture is simulated on the EstiNet simulator, and total time delay, reliability, and satisfaction are used as evaluation parameters. The simulation results are compared with the results of existing architectures, such as software-defined unified virtual monitoring function and ASTP, to analyze the performance of the proposed architecture. The analysis results indicate that our proposed architecture achieves better performance in terms of total time delay (1800 s for 200 IoT devices), reliability (90%), and satisfaction (90%).     

Journey from cloud of things to fog of things: Survey,new trends,and research directions

With the advent of the Internet of Things (IoT) paradigm, the cloud model is unable to offer satisfactory services for latency-sensitive and real-time applications due to high latency and scalability issues. Hence, an emerging computing paradigm named as fog/edge computing was evolved, to offer services close to the data source and optimize the quality of services (QoS) parameters such as latency, scalability, reliability, energy, privacy, and security of data. This article presents the evolution in the computing paradigm from the client-server model to edge computing along with their objectives and limitations. A state-of-the-art review of Cloud Computing and Cloud of Things (CoT) is presented that addressed the techniques, constraints, limitations, and research challenges. Further, we have discussed the role and mechanism of fog/edge computing and Fog of Things (FoT), along with necessitating amalgamation with CoT. We reviewed the several architecture, features, applications, and existing research challenges of fog/edge computing. The comprehensive survey of these computing paradigms offers the depth knowledge about the various aspects, trends, motivation, vision, and integrated architectures. In the end, experimental tools and future research directions are discussed with the hope that this study will work as a stepping-stone in the field of emerging computing paradigms.     

面向延迟敏感型物联网应用的计算迁移策略

针对云计算网络延迟较长、能耗过高和边缘服务器计算资源有限的问题,提出了一种提高延迟敏感型物联网（IoT）应用服务质量（QoS）的边缘-云合作的漂移加惩罚计算迁移策略（DPCO）。首先,建立物联网-边缘-云系统模型,对业务模式、计算任务所经历的传输延迟和计算延迟、系统产生的计算能耗和传输能耗等进行数学建模;然后,以系统能耗和任务平均延迟为优化目标,以边缘服务器的队列稳定性为限制条件构建边缘-云合作的计算迁移优化模型;接着,以优化目标为惩罚函数,基于李雅普诺夫稳定性理论推导出计算迁移优化模型的漂移加惩罚函数特性。最后,基于推导结果提出了DPCO计算迁移算法,通过每时隙选择使当前漂移加惩罚函数最小化的计算迁移策略来降低长期的单位时间能耗和缩短系统平均延迟。与轻流雾处理（LFP）、基准边缘计算（EC）、基准云计算（CC）策略相比,DPCO的系统能耗最低,约是CC策略的2/3;任务平均延迟也最小,可减少为CC的1/5。实验结果表明,DPCO能够有效降低边缘-云计算系统的能量消耗,减少计算任务的端到端延迟,满足延迟敏感型IoT应用的QoS要求。     

A resource oriented integration architecture for the Internet of Things: A business process perspective

The vision of the Internet of Things (IoT) foresees a future Internet incorporating smart physical objects that offer hosted functionality as IoT services. These services when integrated with the traditional enterprise level services form the creation of ambient intelligence for a wide range of applications. To facilitate seamless access and service life cycle management of large, distributed and heterogeneous IoT resources, service oriented computing and resource oriented approaches have been widely used as promising technologies. However, a reference architecture integrating IoT services into either of these two technologies is still an open research challenge. In this article, we adopt the resource oriented approach to provide an end-to-end integration architecture of front-end IoT devices with the back-end business process applications. The proposed architecture promises a programmer friendly access to IoT services, an event management mechanism to propagate context information of IoT devices, a service replacement facility upon service failure, and a decentralized execution of the IoT aware business processes.     

A systematic approach toward security in Fog computing: Assets,vulnerabilities, possible countermeasures

Fog computing is an emerging paradigm in the Internet of Things (IoT) space, consisting of a middle computation layer, sitting between IoT devices and Cloud servers. Fog computing provides additional computing, storage, and networking resources in close proximity to where data is being generated and/or consumed. As the Fog layer has direct access to data streams generated by IoT devices and responses/commands sent from the Cloud, it is in a critical position in terms of security of the entire IoT system. Currently, there is no specific tool or methodology for analysing the security of Fog computing systems in a comprehensive way. Generic security evaluation procedures applicable to most information technology products are time consuming, costly, and badly suited to the Fog context. In this article, we introduce a methodology for evaluating the security of Fog computing systems in a systematic way. We also apply our methodology to a generic Fog computing system, showcasing how it can be purposefully used by security analysts and system designers.     

A bi-objective task scheduling approach in fog computing using hybrid fireworks algorithm

The Journal of Supercomputing - With the rapid increase in the functionality of IoT applications, the services provided by edge/IoT devices have surged in the recent past. Fog computing is gaining...     

Notes on ensembles of IoT,network functions and clouds for service-oriented computing and applications

Many advances have been introduced recently for service-oriented computing and applications (SOCA). The Internet of Things (IoT) has been pervasive in various application domains. Fog/Edge computing models have shown techniques that move computational and analytics capabilities from centralized data centers where most enterprise business services have been located to the edge where most customer’s Things and their data and actions reside. Network functions between the edge and the cloud can be dynamically provisioned and managed through service APIs. Microservice architectures are increasingly used to simplify engineering, deployment and management of distributed services in not only cloud-based powerful machines but also in light-weighted devices. Therefore, a key question for the research in SOCA is how do we leverage existing techniques and develop new ones for coping with and supporting the changes of data and computation resources as well as customer interactions arising in the era of IoT and Fog/Edge computing. In this editorial paper, we attempt to address this question by focusing on the concept of ensembles for IoT, network functions and clouds.     

基于雾计算的计算资源分配方案

雾计算可以为用户提供近距离的数据存储、计算和其他服务，因此雾计算中的任务调度和资源分配已经成为一个新的研究热点。考虑终端用户和雾设备通常处于一种相对开放的状态，扩展了雾计算的体系结构，提出一种开放式雾计算环境中基于稳定匹配的计算资源分配方案，利用雾网络中动态的计算资源协同为用户提供计算服务并收取计算收益，同时终端用户向雾服务器提交任务请求并支付一定的费用。基于稳定匹配的思想，利用子任务的优先级列表、子任务和计算服务设备的偏好列表解决子任务与计算服务设备的分配问题，保证任务的完成时间和计算服务设备的收益。通过实验对方案性能进行了分析，实验结果表明该方案的资源分配时间相对稳定，且在执行雾计算任务时延以及任务违规率上都优于SGA算法和ACOSA算法。     

IoTGuardEye:一种面向物联网服务的Web攻击检测方法

在包括物联网(Internet of Things,IoT)设备的绝大部分边缘计算应用中,基于互联网应用技术(通常被称为Web技术)开发的应用程序接口(Application Programming Interface,API)是设备与远程服务器进行信息交互的核心。相比传统的Web应用,大部分用户无法直接接触到边缘设备使用的API,使得其遭受的攻击相对较少。但随着物联网设备的普及,针对API的攻击逐渐成为热点。因此,文中提出了一种面向物联网服务的Web攻击向量检测方法,用于对物联网服务收到的Web流量进行检测,并挖掘出其中的恶意流量,从而为安全运营中心(Security Operation Center,SOC)提供安全情报。该方法在对超文本传输协议(Hypertext Transfer Protocol,HTTP)请求的文本序列进行特征抽取的基础上,针对API请求的报文格式相对固定的特点,结合双向长短期记忆网络(Bidirectional Long Short-Term Memory,BLSTM)实现对Web流量的攻击向量检测。实验结果表明,相比基于规则的Web应用防火墙(Web Application Firewall,WAF)和传统的机器学习方法,所提方法针对面向物联网服务API的攻击具有更好的识别能力。     

A survey on fog computing for the Internet of Things

Fog computing has emerged to support the requirements of IoT applications that could not be met by today’s solutions. Different initiatives have been presented to drive the development of fog, and much work has been done to improve certain aspects. However, an in-depth analysis of the different solutions, detailing how they can be integrated and applied to meet specific requirements, is still required. In this work, we present a unified architectural model and a new taxonomy, by comparing a large number of solutions. Finally, we draw some conclusions and guidelines for the development of IoT applications based on fog.     

Research on the collaboration of service selection and resource scheduling for IoT simulation workflows

Cloud resources provide a promising way to efficiently perform the needed simulation tasks for a complex manufacturing process. Most of the existing work focuses only on how to effectively schedule computing resources to execute computing requirements of simulation workflows in Internet of Things (IoT) applications. Research on the scheduling of simulation workflows in consideration of task ordering, service selection, and resource allocation altogether has not been lacking. To fill in this void, this paper proposes a cloud-based 3-stage workflow scheduling model. Before scheduling computing resources to complete task requirements, the order of the tasks is determined and the services that can meet the task requirements are selected. In this model, the workload to satisfy task requirements is not fixed and takes on a different value depending upon the service selected with its unique complexity and accuracy. An optimization function that transforms and integrates makespan, cost, and accuracy in a unique way is proposed. For its solution, the relatively new symbiotic organisms search (SOS) algorithm is modified and two SOS-based optimization strategies are developed, i.e., joint optimization-based SOS (JOSOS) and split optimization-based SOS (SOSOS). The simulation results reveal that SOS-based algorithms, especially the SOSOS method, outperform all compared algorithms. Based on the proposed method, simulation services and computing resources can be rationally selected and scheduled to ensure the requirements of IoT applications.     

CyberGuarder: A virtualization security assurance architecture for green cloud computing

As the sizes of IT infrastructure continue to grow, cloud computing is a natural extension of virtualisation technologies that enable scalable management of virtual machines over a plethora of physically connected systems. The so-called virtualisation-based cloud computing paradigm offers a practical approach to green IT/clouds, which emphasise the construction and deployment of scalable, energy-efficient network software applications (NetApp) by virtue of improved utilisation of the underlying resources. The latter is typically achieved through increased sharing of hardware and data in a multi-tenant cloud architecture/environment and, as such, accentuates the critical requirement for enhanced security services as an integrated component of the virtual infrastructure management strategy. This paper analyses the key security challenges faced by contemporary green cloud computing environments, and proposes a virtualisation security assurance architecture, CyberGuarder, which is designed to address several key security problems within the ‘green’ cloud computing context. In particular, CyberGuarder provides three different kinds of services; namely, a virtual machine security service, a virtual network security service and a policy based trust management service. Specifically, the proposed virtual machine security service incorporates a number of new techniques which include (1) a VMM-based integrity measurement approach for NetApp trusted loading, (2) a multi-granularity NetApp isolation mechanism to enable OS user isolation, and (3) a dynamic approach to virtual machine and network isolation for multiple NetApp’s based on energy-efficiency and security requirements. Secondly, a virtual network security service has been developed successfully to provide an adaptive virtual security appliance deployment in a NetApp execution environment, whereby traditional security services such as IDS and firewalls can be encapsulated as VM images and deployed over a virtual security network in accordance with the practical configuration of the virtualised infrastructure. Thirdly, a security service providing policy based trust management is proposed to facilitate access control to the resources pool and a trust federation mechanism to support/optimise task privacy and cost requirements across multiple resource pools. Preliminary studies of these services have been carried out on our iVIC platform, with promising results. As part of our ongoing research in large-scale, energy-efficient/green cloud computing, we are currently developing a virtual laboratory for our campus courses using the virtualisation infrastructure of iVIC, which incorporates the important results and experience of CyberGuarder in a practical context.     

国家高性能计算环境资源评价规范研究

高性能计算资源作为科技创新的重要手段,是当代科技竞争的战略制高点,能集中体现一个国家的综合实力。国家高性能计算环境聚合了国内优秀的高性能计算资源,面向用户提供高效、便捷的高性能计算服务。为加强环境建设、提高服务质量,本文提出了一套可以量化网络服务水平和集群计算服务水平的规范,为高性能计算环境的准入提供理论依据,支持和引导用户合理使用资源,形成全局统筹的资源布局。本文首先提出对高性能计算资源服务水平的评价标准,针对资源的性能、可用性、安全性、可靠性、需求管理、技术支持和服务响应这些内容分别展开介绍。然后介绍了这些评价标准的计算方法,为评价标准的确立提供理论基础。最后以提出的资源评价标准为依据,对资源的分级标准进行制定并提出高性能计算环境的准入标准。     

雾计算中支持外包与撤销的属性基加密方案

雾计算将云计算的计算能力、数据分析应用等扩展到网络边缘,可满足物联网设备的低时延、移动性等要求,但同时也存在数据安全和隐私保护问题。传统云计算中的属性基加密技术不适用于雾环境中计算资源有限的物联网设备,并且难以管理属性变更。为此,提出一种支持加解密外包和撤销的属性基加密方案,构建“云-雾-终端”的三层系统模型,通过引入属性组密钥的技术,实现动态密钥更新,满足雾计算中属性即时撤销的要求。在此基础上,将终端设备中部分复杂的加解密运算外包给雾节点,以提高计算效率。实验结果表明,与KeyGen、Enc等方案相比,该方案具有更优的计算高效性和可靠性。     

一种基于遗传算法的多站点协同计算卸载算法

边缘计算为资源受限的物联网IoT设备扩展计算资源、增强存储容量,可以改善IoT应用程序的执行性能。在IoT环境中,大多数应用都将以分布式架构的形式部署在各站点中,站点之间需要协作完成任务。为了解决物联网环境中多站点协同计算的代价优化问题,提出了一种基于遗传算法的多站点协同计算卸载算法GAMCCO。该算法将应用程序抽象为任务依赖关系图模型,分析各任务之间的依赖关系,将多站点协同计算卸载的问题建模为代价模型,并利用遗传算法寻找最小代价的卸载方案。实验与评估结果表明,所提出的GAMCCO算法可以有效减少IoT应用的时延,同时降低终端设备的能耗。     

动态物联网环境下的联盟学习计算卸载优化

智能城市、智慧工厂等对物联网设备（Internet of Things，IoT）的性能和连接性提出了挑战。边缘计算的出现弥补了这些能力受限的设备，通过将密集的计算任务从它们迁移到边缘节点（Edge Node，EN），物联网设备能够在节约更多能耗的同时，仍保持服务质量。计算卸载决策涉及协作和复杂的资源管理，应该根据动态工作负载和网络环境实时确定计算卸载决策。采用模拟实验的方法，通过在物联网设备和边缘节点上都部署深度强化学习代理来最大化长期效用，并引入联盟学习来分布式训练深度强化学习代理。首先构建支持边缘计算的物联网系统，IoT从EN处下载已有模型进行训练，密集型计算任务卸载至EN进行训练；IoT上传更新的参数至EN，EN聚合该参数与EN处的模型得到新的模型；云端可在EN处获得新的模型并聚合，IoT也可以从EN获得更新的参数应用在设备上。经过多次迭代，该IoT能获得接近集中式训练的性能，并且降低了物联网设备和边缘节点之间的传输成本，实验证实了决策方案和联盟学习在动态物联网环境中的有效性。