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Fog Computing: A Comprehensive Literature Review (Other (Not Listed) Sample)

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this is a comprehensive literature review paper that explores the cloud-based fog computing paradigm. the paper is formatted in IEEE standard and it seeks to help users understand how fog computing differs from cloud computing. additionally, some of the popular application areas of fog computing are also discussed in the paper.

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Fog Computing: A Comprehensive Literature Review
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Abstract
The introduction of fog computing is necessitated by the considerable drawbacks associated with cloud computing architectures. Cloud computing refers to the process of running computer services and resources such as memory in remote servers hosted by third-parties. The cloud paradigm has been widely used by many business organizations that offer web-based services through mobile applications. Though cloud computing provides more efficient services than the on-site servers, the architecture is gradually developing latency issues, making significant services unavailable to clients. Cloud computing largely relies on the internet service provider’s bandwidth, making it unsuitable for consumers that transmit data that exceeds the available bandwidth. This has compelled organizations to enhance their reputation amongst consumers by improving service availability by adopting the fog computing technique. Fog computing is a paradigm that extends Cloud computing and services to the edge of the network. Fog computing can be achieved by deploying nodes in the existing cloud architecture to enhance resource management in different cloud computing models. Cloud architectures are usually classified according to how the resources are shared and the type of users that access the remote servers. Public clouds are prone to high latency and service unavailability, which necessitates the need to deploy fog nodes between the interconnected smart devices that request resources from cloud servers frequently. It mainly places processes and resources at the edge of the cloud, often on network devices, while data remains stored in the cloud. This paper presents a comprehensive literature review of the fog computing paradigm by analyzing the architecture, networking protocols, security layers, and applications of the IoT-based fogging. The paper compares literary works, mainly comprising of peer-reviewed journals to highlight the significant aspects of fog computing from different perspectives. The literature review also discusses considerable cloud computing drawbacks that triggered the inception of fog computing. The commonly used cloud computing models are also discussed to give an overview of how consumers usually access various remote services from this architecture. The paper upholds that fog computing is a consumer-centric paradigm that strives to enhance service availability by providing the maximum bandwidth required for efficient data transmission.
Keywords: Fog computing, cloud computing, cloudlets, Application Programming Interface (API).
* INTRODUCTION
Fog computing is a branch of Internet of Things (IoT) technology that seeks to improve the cloud computing architecture by enhancing service efficiency [1]. The main ideology behind fog computing is to bring more services closer to consumers and increase the bandwidth to support robust data exchange between the interconnected IoT devices. It strives to overcome the limitation experienced in the end devices by availing timely applications through effective communication, sufficient memory resources, and computational capabilities. Fog computing empowers IoT-based applications such as virtual reality, smart cities, and augmented reality. The current mobile devices that run various internet applications generate robust data that require faster processing. Though fog computing paradigm strives to complements the cloud architecture, new strategies should be deployed to ensure that resources are managed effectively through the enforcement of the QoS (Quality of Service) by the vendors that provide IoT-based smart devices [2]. This can be achieved by using machine learning techniques to check whether the devices meet their QoS requirements.
24765002286001866900-9525Increased latency00Increased latency
-961707376237Cloud data center00Cloud data center52387513970CLOUD SERVER00CLOUD SERVER
17335511327150049530094615
141922599060Resource 200Resource 2-190500118110Resource 100Resource 1
638175265430
28575079375119062541275196215069850-390525155575
1714500160020Fog00Fog-3810064769Fog00Fog
-363855227965Fog Nodes00Fog Nodes742950261620Fog00Fog
-409575335280Smart TV00Smart TV1695450249555Smartphone00Smartphone-400050192405
29527563500Smart home00Smart home11048996350Laptop00Laptop
6476991250940011525251536690001060451552575144145
438150148590Edge Devices00Edge Devices
187642576835Reduced latency00Reduced latency
Figure 1: Fog Computing Architecture
* The Standard Cloud Computing Models
Cloud computing services are classified according to their flexibility, architecture, and mode of service. The primary goal for consumers that pay for cloud computing is to get the services; hence, most models have the ‘as a Service (aaS)’ phrase as their suffix. The various cloud computing models include: DBaaS (database), DRaaS (disaster recovery), NaaS (networking), SECaaS (security), SaaS (storage), MBaaS (mobile backend), and FaaS (function) [3]. However, all these falls under either of the three major cloud computing models, as discussed below:
* Platform-as-a-Service (PaaS) Model
Developers usually use PaaS as it often comes with content management systems, an operating system, database, development tools, and a server that an organization can utilize to configure its web applications [3]. The PaaS model offers a computing platform based on specific tools, programming languages, and applications. It is convenient for organizations that prefer writing, deploying, and running their business applications independently. Leading vendors include Microsoft Azure, Google App Engine, Amazon Web Services, and IBM Blue mix.
* Software-as-a-Service (SaaS) Model
The SaaS model grants its consumers access to various applications and software on a monthly or annual subscription basis. User data is usually stored in the vendor’s cloud application and can be accessed from any internet-enabled device. The service provider is in charge of all the software upgrades, and consumers only get to access the application [3]. The leading vendors that offer SaaS include Cisco, Salesforce.com, Oracle, Microsoft, Google, Microsoft, IBM, SAP, Intuit, and ADP.
* Infrastructure-as-a-Service (IaaS) Model
In the IaaS model, service providers offer computing infrastructures such as storage units, server-less computing, containers, networks, and virtual computers [3]. It is similar to using networking devices or servers, only that IaaS is managed remotely. Amazon Web Services dominate the list of IaaS vendors. The other vendors are Microsoft Azure, IBM, SoftLayer, and Google Cloud. Firms such as NetApp, Cisco, HPE, Lenovo, and Dell provide the necessary technologies used by big organizations to set up their private IaaS services.
Cloud services can also be classified according to how users deploy the models in various setups. This may include small-scale consumer-based or large-scale enterprise architectures. Consumers can either opt for public, private, or hybrid deployment models that suit their specific individual and organizational needs.
* THE FOGGING FRAMEWORK
* Characteristics of Fog Computing
The primary idea behind the inception of fog computing was to complement the cloud computing models. This involves extending the cloud’s features and increase service availability in IoT devices at the edge level. All the fog computing features are designed towards improving the Quality of Service (QoS) in IoT devices, better bandwidth for big data analysis, and reduced latency at the edge level where devices require timely resources [4]. This framework facilitates rapid development of new services and applications in the IoT paradigm, which can be described by the following distinct fog characteristics:
* Cognition
This refers to the architectures attentiveness and response rate to the users’ requests and objectives. Data analytics and access parameters are modeled towards enhancing the overall user experience in terms of data transmission, storage, and access [4]. These features give clients better control over their data, which is necessary for service excellence. The close proximity of web-based applications to the edge devices also enables the IoT framework to identify and address specific user requirements in the wireless architecture.
* Real-Time Interactions
This is achieved through real-world experiences such as the deployment of sensors to monitor traffic, live surveillance, and real-time monitoring of industrial processes at enterprise levels [4, 5].
* Distribution of the Geographical Environment
Fog computing paradigm facilitates an extended deployment of static and mobile edge devices to enhance their Quality of Service. The sensor nodes are usually deployed in close proximities to facilitate data capturing in repetitive processes such as the weather patterns and fingerprint scanners [5].
* Heterogeneity
Since fog computing mainly provides virtual services to the edge devices, its architecture comprise of multiple hierarchical building blocks deployed in different locations evenly. The equal distribution of these fog nodes ensures that essential resources such as memory, computational power, and other functions are readily available at the lower level.
* Widespread Deployment of Wireless Sensor Networks
Cellular mobile gateways and wireless access pro...

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