Fog Computing and Its Challenges

Fog Computing and Its Challenges

Vighnesh Srinivasa Balaji (Ramaiah Institute of Technology, India)
Copyright: © 2019 |Pages: 17
DOI: 10.4018/978-1-5225-6070-8.ch002
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In recent times, the number of internet of things (IoT) devices/sensors increased tremendously. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named fog computing has been introduced. In this chapter, the authors will introduce fog computing, its difference in comparison to cloud computing, and issues related to fog. Among the three issues (i.e. service, structural, and security issues), this chapter scrutinizes and comprehensively discusses the service and structural issues also providing the service level objectives of the fog. They next provide various algorithms for computing in fog, the challenges faced, and future research directions. Among the various uses of fog, two scenarios are put to use.
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Difference Between Fog And Cloud

Cloud computing is usually a model for enabling convenient, on-demand network use of a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that may be rapidly provisioned and released with minimal management effort or vendor interaction. Cloud is located within the network with various topologies, speeds and no central control due to which there are a few qualities of service factors unresolved. One such issue is latency, as many applications require real time data processing, and services provided by the cloud cannot satisfy these requirements. Another such a problem is security and privacy. In the internet today, the applications are located far off from the service providers and so depending on the number of intermediate nodes the data moves through public cloud thus compromising confidentiality and integrity of the data as specified in Figure 1.

Fog computing was introduced by CiscoSystems as new a model to ease wireless data transfer to distributed devices in the Internet of Things (IoT) network paradigm. Fog Computing acts as a paradigm that extends Cloud computing and brings its related services to the network edge. Fog, similar to Cloud, provides data, compute, storage, and application services to end-users. The characteristics distinguishing Fog are its dense geographical distribution, its proximity to end-users, and its support for mobility. By doing so, it improves QoS and also reduces latency, increases its mobility which supports the internet of everything (IoE). Thanks to its wide geographical distribution the Fog paradigm is well positioned for real time big data and real time analytics. Fog supports densely distributed data collection points, hence adding a fourth axis to the often-mentioned Big Data dimensions (volume, variety, and velocity).

Figure 1.

Confidentiality and integrity of the data


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