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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.

物聯網(IoT)可以促進創新,提高生活質量,但它產生了前所未有的海量數據,這些數據對于傳統系統、云計算甚至邊緣計算來說都難以處理。

INTRODUCTION

介紹

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.

物聯網有望使許多產品(包括消費電子設備、家用電器、醫療設備、相機和所有類型的傳感器)成為互聯網環境的一部分。這為促進物與人之間的新互動打開了大門,并使能提高生活質量的智能城市、基礎設施和服務得以實現。
研究人員估計,到2025年,物聯網可能產生每年11萬億美元的經濟影響(包括產生的收入和節省),這將占全球經濟的11%左右;用戶將部署1萬億物聯網設備。



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.

最近對一個擁有3000萬用戶的醫療相關物聯網應用程序的分析顯示,數據流高達每秒25000組。在擁有更多數據源的智能城市中,實時數據流可以很容易地達到每秒數百萬組。

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.

為了解決這些問題,邊緣計算被提出來了,利用物聯網傳感器附近的計算資源進行本地存儲和初步數據處理。這將減少網絡擁塞,并加快分析和決策。然而,邊緣設備的資源有限,無法滿足多個物聯網應用程序的需求,這會導致資源的爭用和處理延遲的增加。

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.

霧計算——無縫集成邊緣設備和云資源——有助于克服這些局限性。它通過利用云資源以及配合使用在地理上分布式的邊緣設備,避免了邊緣處的資源爭用。

FOG COMPUTING CHARACTERISTICS

霧計算的特點

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.

霧計算是一種分布式計算范式,它向網絡邊緣提供類似云的服務。如圖1所示,它利用的是云和邊緣資源以及自己的基礎設施。本質上,該技術通過利用客戶端或用戶附近的邊緣設備進行大量存儲、通信、控制、配置和管理,在本地處理物聯網數據。這種方法得益于邊緣設備與傳感器的緊密接觸,同時利用了云資源的按需可伸縮性。



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.

圖1:霧計算環境中的分布式數據處理。基于系統所需的功能,用戶可以在不同的環境中部署物聯網傳感器,包括道路、醫療中心和農場。一旦系統從傳感器收集到信息,包括附近網關和私有云在內的霧設備就會動態地進行數據分析。

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.

霧計算包含了在分布式云和邊緣設備中運行的數據處理或分析應用程序的組件。它也有利于對數據中心和終端設備之間的計算、網絡和存儲服務進行管理和規劃。此外,它支持用戶移動性、資源和接口異構性以及分布式數據分析,以滿足需要低延遲的廣泛分布式應用程序的需求。

FOG-COMPUTING COMPONENTS

霧計算的組成

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.

圖2給出了一個霧計算參考架構。霧系統通常使用感知過程驅動和流處理這兩種編程模型。傳感器將數據傳輸到物聯網,在霧設備上運行的應用程序訂閱并處理這些信息,并將它們轉化為發送給執行器的操作。



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.

圖2:霧計算架構。底層是終端設備(包括傳感器和執行器)以及增強其功能的應用程序。它們使用下一層(網絡層)與邊緣設備(如網關)通信,然后與云服務通信。資源管理層運行整個基礎設施,并啟動基于服務質量的任務執行。最后,應用程序利用霧計算編程模型向用戶交付智能服務。



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

有四個著名的軟件系統用于構建霧計算環境和應用程序。

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提供設備管理,并在霧計算環境中提供M2M服務。使用思科IOx API提供的設備抽象,在霧設備上運行的應用程序可以通過M2M協議與其他物聯網設備進行通信。

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)支持在網絡邊緣進行數據管理和分析,它內置在思科系統及其合作伙伴提供的產品中。

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的霧計算平臺是安裝在智能電網中的網絡設備上的軟件。它在設備和數據處理服務之間提供可靠的M2M通信,而無需通過云端。

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

思科ParStream的霧計算平臺支持實時物聯網分析。

FOG-COMPUTING APPLICATIONS 霧計算的應用
Various applications could benefit from fog computing.

各種應用都可以從霧計算中獲益。

1. Healthcare and activity tracking

醫療和活動跟蹤

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.

霧計算在醫療保健中可能很有用,因為在醫療保健中,實時的處理和事件響應是至關重要的。研究人員提出了一個系統,利用霧計算來檢測、預測和預防中風患者跌倒。跌倒檢測學習算法是跨邊緣和云資源動態部署的。實驗表明,該系統的響應時間較低,能耗也較低。

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

研究人員還提出了一種基于霧計算的智能醫療系統,能夠實現低延遲、支持移動性、感知設備位置。

2. Smart utility services

智能公共設施服務



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,構建一個真實的物聯網環境作為評估這些技術的測試平臺是很昂貴的,并且不能為進行可重復實驗提供一個可控的環境。為了克服這個限制,我們開發了一個名為iFogSim的開源模擬器。iFogSim支持對霧計算環境進行建模和仿真,以便在多種場景下評估跨邊緣和云資源的資源管理和調度策略對延遲、能耗、網絡擁塞和操作成本的影響。它測量性能指標,并模擬邊緣設備、云數據中心、傳感器、網絡連接、數據流和流處理應用程序的狀態。

CHALLENGES

霧計算面臨的挑戰

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.

要實現霧計算的全部潛力,目前還面臨著一些挑戰,包括平衡邊緣和云資源之間的負載分配、API(應用程序編程接口)和服務的管理與共享、以及SDN(軟件定義網絡)通信。還有其他幾個重要的例子。

1. Enabling real-time analytics

對實時分析的支持

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.

在霧計算環境中,資源管理系統應該能夠動態地確定哪些分析任務被推送到哪些基于云或邊緣的資源,從而最小化延遲和最大化吞吐量。這些系統還必須考慮其他標準,例如各國涉及醫療和金融信息的數據隱私法。

2. Programming models and architectures

對模型和架構的規劃

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.

大多數流處理和數據處理框架沒有為霧環境和物聯網環境提供足夠的可伸縮性和靈活性,因為它們的架構是基于靜態配置的。霧環境需要有動態添加和刪除資源的能力,因為處理節點通常是移動設備,而它們連接和離開網絡很頻繁。

3. Security, reliability, and fault tolerance

安全性、可靠性和容錯能力



CONCLUSIONS

總結

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.

霧計算將邊緣和云資源進行了無縫集成。它支持對物聯網傳感器產生的前所未有的數據進行分散和智能處理。
霧計算可以為社會帶來許多好處,例如應用于智能醫療。因此,霧計算的進一步發展可以幫助物聯網發揮其巨大的潛力。