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[科普知识] (IEEE论文)雾计算:帮助物联网实现其潜力

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发表于 2019-6-6 11:40:00 | 显示全部楼层 |阅读模式
本帖最后由 龙腾网翻译版务 于 2019-6-6 16:09 编辑
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Abstract

摘要



The Internet of Things (IoT) could enable innovations that enhance the quality of life, but it generates unprecedented amounts of data that are difficult for traditional systems, the cloud, and even edge computing to handle. Fog computing is designed to overcome these limitations.

物联网(IoT)可以促进创新,提高生活质量,但它产生了前所未有的海量数据,这些数据对于传统系统、云计算甚至边缘计算来说都难以处理。

INTRODUCTION

介绍

The Internet of Things (IoT) promises to make many items—including consumer electronic devices, home appliances, medical devices, cameras, and all types of sensors—part of the Internet environment. This opens the door to innovations that facilitate new interactions among things and humans, and enables the realization of smart cities, infrastructures, and services that enhance the quality of life.
By 2025, researchers estimate that the IoT could have an economic impact—including, for example, revenue generated and operational savings—of $11 trillion per year, which would represent about 11 percent of the world economy; and that users will deploy 1 trillion IoT devices.

物联网有望使许多产品(包括消费电子设备、家用电器、医疗设备、相机和所有类型的传感器)成为互联网环境的一部分。这为促进物与人之间的新互动打开了大门,并使能提高生活质量的智能城市、基础设施和服务得以实现。
研究人员估计,到2025年,物联网可能产生每年11万亿美元的经济影响(包括产生的收入和节省),这将占全球经济的11%左右;用户将部署1万亿物联网设备。



Recent analysis of a healthcare-related IoT application with 30 million users showed data flows up to 25,000 tuples per second. And real-time data flows in smart cities with many more data sources could easily reach millions of tuples per second.

最近对一个拥有3000万用户的医疗相关物联网应用程序的分析显示,数据流高达每秒25000组。在拥有更多数据源的智能城市中,实时数据流可以很容易地达到每秒数百万组。

To address these issues, edge computing was proposed to use computing resources near IoT sensors for local storage and preliminary data processing. This would decrease network congestion, as well as accelerate analysis and the resulting decision making. However, edge devices can’t handle multiple IoT applications competing for their limited resources, which results in resource contention and increases processing latency.

为了解决这些问题,边缘计算被提出来了,利用物联网传感器附近的计算资源进行本地存储和初步数据处理。这将减少网络拥塞,并加快分析和决策。然而,边缘设备的资源有限,无法满足多个物联网应用程序的需求,这会导致资源的争用和处理延迟的增加。

Fog computing—which seamlessly integrates edge devices and cloud resources—helps overcome these limitations. It avoids resource contention at the edge by leveraging cloud resources and coordinating the use of geographically distributed edge devices.

雾计算——无缝集成边缘设备和云资源——有助于克服这些局限性。它通过利用云资源以及配合使用在地理上分布式的边缘设备,避免了边缘处的资源争用。

FOG COMPUTING CHARACTERISTICS

雾计算的特点

Fog computing is a distributed paradigm that provides cloud-like services to the network edge. It leverages cloud and edge resources along with its own infrastructure, as Figure 1 shows. In essence, the technology deals with IoT data locally by utilizing clients or edge devices near users to carry out a substantial amount of storage, communication, control, configuration, and management. The approach benefits from edge devices’ close proximity to sensors, while leveraging the on-demand scalability of cloud resources.

雾计算是一种分布式计算范式,它向网络边缘提供类似云的服务。如图1所示,它利用的是云和边缘资源以及自己的基础设施。本质上,该技术通过利用客户端或用户附近的边缘设备进行大量存储、通信、控制、配置和管理,在本地处理物联网数据。这种方法得益于边缘设备与传感器的紧密接触,同时利用了云资源的按需可伸缩性。



Figure 1. Distributed data processing in a fog-computing environment. Based on the desired functionality of a system, users can deploy Internet of Things (IoT) sensors in different environments including roads, medical centers, and farms. Once the system collects information from the sensors, fog devices—including nearby gateways and private clouds— dynamically conduct data analytics.

图1:雾计算环境中的分布式数据处理。基于系统所需的功能,用户可以在不同的环境中部署物联网传感器,包括道路、医疗中心和农场。一旦系统从传感器收集到信息,包括附近网关和私有云在内的雾设备就会动态地进行数据分析。

Fog computing involves the components of data-processing or analytics applications running in distributed cloud and edge devices. It also facilitates the management and programming of computing, networking, and storage services between datacenters and end devices. In addition, it supports user mobility, resource and interface heterogeneity, and distributed data analytics to address the requirements of widely distributed applications that need low latency.

雾计算包含了在分布式云和边缘设备中运行的数据处理或分析应用程序的组件。它也有利于对数据中心和终端设备之间的计算、网络和存储服务进行管理和规划。此外,它支持用户移动性、资源和接口异构性以及分布式数据分析,以满足需要低延迟的广泛分布式应用程序的需求。

FOG-COMPUTING COMPONENTS

雾计算的组成

Figure 2 presents a fog-computing reference architecture. Fog systems generally use the sense-process-actuate and stream-processing programming models. Sensors stream data to IoT networks, applications running on fog devices subscribe to and process the information, and the obtained insights are translated into actions sent to actuators.

图2给出了一个雾计算参考架构。雾系统通常使用感知过程驱动和流处理这两种编程模型。传感器将数据传输到物联网,在雾设备上运行的应用程序订阅并处理这些信息,并将它们转化为发送给执行器的操作。



Figure 2. Fog-computing architecture. In the bottom layer are end devices— including sensors and actuators—along with applications that enhance their functionality. These elements use the next layer, the network, for communicating with edge devices, such as gateways, and then with cloud services. The resource-management layer runs the entire infrastructure and enables quality-of-service enforcement. Finally, applications leverage fog-computing programming models to deliver intelligent services to users.

图2:雾计算架构。底层是终端设备(包括传感器和执行器)以及增强其功能的应用程序。它们使用下一层(网络层)与边缘设备(如网关)通信,然后与云服务通信。资源管理层运行整个基础设施,并启动基于服务质量的任务执行。最后,应用程序利用雾计算编程模型向用户交付智能服务。



There are four prominent software systems for building fog computing environments and applications.

有四个著名的软件系统用于构建雾计算环境和应用程序。

Cisco IOx provides device management and enables M2M services in fog environments. Using device abstractions provided by Cisco IOx APIs, applications running on fog devices can communicate with other IoT devices via M2M protocols.

思科IOx提供设备管理,并在雾计算环境中提供M2M服务。使用思科IOx API提供的设备抽象,在雾设备上运行的应用程序可以通过M2M协议与其他物联网设备进行通信。

Cisco Data in Motion (DMo) enables data management and analysis at the network edge and is built into products that Cisco Systems and its partners provide.

思科的Data in Motion (DMo)支持在网络边缘进行数据管理和分析,它内置在思科系统及其合作伙伴提供的产品中。

LocalGrid’s fog-computing platform is software installed on network devices in smart grids. It provides reliable M2M communication between devices and data-processing services without going through the cloud.

LocalGrid的雾计算平台是安装在智能电网中的网络设备上的软件。它在设备和数据处理服务之间提供可靠的M2M通信,而无需通过云端。

Cisco ParStream’s fog- computing platform enables real-time IoT analytics.

思科ParStream的雾计算平台支持实时物联网分析。

FOG-COMPUTING APPLICATIONS 雾计算的应用
Various applications could benefit from fog computing.

各种应用都可以从雾计算中获益。

1. Healthcare and activity tracking

医疗和活动跟踪

Fog computing could be useful in healthcare, in which real-time processing and event response are critical. One proposed system utilizes fog computing to detect, predict, and prevent falls by stroke patients.The fall-detection learning algorithms are dynamically deployed across edge devices and cloud resources. Experiments concluded that this system had a lower response time and consumed less energy than cloud-only approaches.

雾计算在医疗保健中可能很有用,因为在医疗保健中,实时的处理和事件响应是至关重要的。研究人员提出了一个系统,利用雾计算来检测、预测和预防中风患者跌倒。跌倒检测学习算法是跨边缘和云资源动态部署的。实验表明,该系统的响应时间较低,能耗也较低。

A proposed fog computing-based smart-healthcare system enables low latency, mobility support, and location and privacy awareness.

研究人员还提出了一种基于雾计算的智能医疗系统,能够实现低延迟、支持移动性、感知设备位置。

2. Smart utility services

智能公共设施服务



However, constructing a real IoT environment as a testbed for uating such techniques is costly and doesn’t provide a controllable environment for conducting repeatable experiments. To overcome this limitation, we developed an open source simulator called iFogSim. iFogSim enables the modeling and simulation of fog-computing environments for the uation of resource-management and scheduling policies across edge and cloud resources under multiple scenarios, based on their impact on latency, energy consumption, network congestion, and operational costs. It measures performance metrics and simulates edge devices, cloud datacenters, sensors, network links, data streams, and stream-processing applications.

然而,构建一个真实的物联网环境作为评估这些技术的测试平台是很昂贵的,并且不能为进行可重复实验提供一个可控的环境。为了克服这个限制,我们开发了一个名为iFogSim的开源模拟器。iFogSim支持对雾计算环境进行建模和仿真,以便在多种场景下评估跨边缘和云资源的资源管理和调度策略对延迟、能耗、网络拥塞和操作成本的影响。它测量性能指标,并模拟边缘设备、云数据中心、传感器、网络连接、数据流和流处理应用程序的状态。

CHALLENGES

雾计算面临的挑战

Realizing fog computing’s full potential presents several challenges including balancing load distribution between edge and cloud resources, API and service management and sharing, and SDN communications. There are several other important examples.

要实现雾计算的全部潜力,目前还面临着一些挑战,包括平衡边缘和云资源之间的负载分配、API(应用程序编程接口)和服务的管理与共享、以及SDN(软件定义网络)通信。还有其他几个重要的例子。

1. Enabling real-time analytics

对实时分析的支持

In fog environments, resource management systems should be able to dynamically determine which analytics tasks are being pushed to which cloud- or edge-based resource to minimize latency and maximize throughput. These systems also must consider other criteria such as various countries’ data privacy laws involving, for example, medical and financial information.

在雾计算环境中,资源管理系统应该能够动态地确定哪些分析任务被推送到哪些基于云或边缘的资源,从而最小化延迟和最大化吞吐量。这些系统还必须考虑其他标准,例如各国涉及医疗和金融信息的数据隐私法。

2. Programming models and architectures

对模型和架构的规划

Most stream- and data-processing frameworks don’t provide enough scalability and flexibility for fog and IoT environments because their architecture is based on static configurations. Fog environments require the ability to add and remove resources dynamically because processing nodes are generally mobile devices that frequently join and leave networks.

大多数流处理和数据处理框架没有为雾环境和物联网环境提供足够的可伸缩性和灵活性,因为它们的架构是基于静态配置的。雾环境需要有动态添加和删除资源的能力,因为处理节点通常是移动设备,而它们连接和离开网络很频繁。

3. Security, reliability, and fault tolerance

安全性、可靠性和容错能力



CONCLUSIONS

总结

Fog computing enables the seamless integration of edge and cloud resources. It supports the decentralized and intelligent processing of unprecedented data volumes generated by IoT sensors deployed for smooth integration of physical and cyber environments.
This could generate many benefits to society by, for example, enabling smart healthcare applications. The further development of fog computing could thus help the IoT reach its vast potential.

雾计算将边缘和云资源进行了无缝集成。它支持对物联网传感器产生的前所未有的数据进行分散和智能处理。
雾计算可以为社会带来许多好处,例如应用于智能医疗。因此,雾计算的进一步发展可以帮助物联网发挥其巨大的潜力。



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