Clarifying Fog Computing and Networking: 10 Questions and Answers

Chiang, Mung; Ha, Sangtae; Chih‐Lin, I; Risso, Fulvio; Zhang, Tao

doi:10.1109/mcom.2017.7901470

Cited by 209 publications

(101 citation statements)

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“…Among directions for future work, we mention here the analysis of more general caching strategies that allow for coding and arbitrary caching patterns across files and ENs; of the impact of imperfect CSI; of partial connectivity (see [28] and references therein); and of more realistic request models [22]; as well as the investigation of online caching within large fog architectures including metro and core segments [29].…”

Section: Discussionmentioning

confidence: 99%

Online Edge Caching and Wireless Delivery in Fog-Aided Networks With Dynamic Content Popularity

Azimi

Simeone

Sengupta

et al. 2018

IEEE J. Select. Areas Commun.

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Fog Radio Access Network (F-RAN) architectures can leverage both cloud processing and edge caching for content delivery to the users. To this end, F-RAN utilizes caches at the edge nodes (ENs) and fronthaul links connecting a cloud processor to ENs. Assuming time-invariant content popularity, existing information-theoretic analyses of content delivery in F-RANs rely on offline caching with separate content placement and delivery phases. In contrast, this work focuses on the scenario in which the set of popular content is time-varying, hence necessitating the online replenishment of the ENs' caches along with the delivery of the requested files. The analysis is centered on the characterization of the long-term Normalized Delivery Time (NDT), which captures the temporal dependence of the coding latencies accrued across multiple time slots in the high signal-to-noise ratio regime. Online edge caching and delivery schemes are investigated for both serial and pipelined transmission modes across fronthaul and edge segments. Analytical results demonstrate that, in the presence of a time-varying content popularity, the rate of fronthaul links sets a fundamental limit to the long-term NDT of F-RAN system. Analytical results are further verified by numerical simulation, yielding important design insights. Index TermsEdge caching, Online caching, C-RAN, F-RAN, interference management, fog networking, 5G.S. M. Azimi is with the CWiP,

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Section: Discussionmentioning

confidence: 99%

Online Edge Caching and Wireless Delivery in Fog-Aided Networks With Dynamic Content Popularity

Azimi

Simeone

Sengupta

et al. 2018

IEEE J. Select. Areas Commun.

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“…They introduce various applications that benefit from fog and present several challenges. More recently, Chiang et al [18] provide a tutorial on fog computing. They discuss at a very high level the differences between fog computing, edge computing, and cloud computing.…”

Section: A Existing Surveys and Tutorials On Fog Computingmentioning

confidence: 99%

“…Table VIII provides a summary of these challenges and research directions, the relevant works from the literature, and potential solutions and starting points. 18 snia.org/cdmi This paper has been accepted for publication in IEEE Communications Surveys & Tutorials. The content is final but has NOT been proof-read.…”

Section: B Algorithmic Challenges and Research Directionsmentioning

confidence: 99%

A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges

Mouradian

Naboulsi

Yangui

et al. 2018

IEEE Commun. Surv. Tutorials

833

405

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Cloud computing with its three key facets (i.e., IaaS, PaaS, and SaaS) and its inherent advantages (e.g., elasticity and scalability) still faces several challenges. The distance between the cloud and the end devices might be an issue for latencysensitive applications such as disaster management and content delivery applications. Service Level Agreements (SLAs) may also impose processing at locations where the cloud provider does not have data centers. Fog computing is a novel paradigm to address such issues. It enables provisioning resources and services outside the cloud, at the edge of the network, closer to end devices or eventually, at locations stipulated by SLAs. Fog computing is not a substitute for cloud computing but a powerful complement. It enables processing at the edge while still offering the possibility to interact with the cloud. This article presents a comprehensive survey on fog computing. It critically reviews the state of the art in the light of a concise set of evaluation criteria. We cover both the architectures and the algorithms that make fog systems. Challenges and research directions are also introduced. In addition, the lessons learned are reviewed and the prospects are discussed in terms of the key role fog is likely to play in emerging technologies such as Tactile Internet.

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“…Chiang et al [43] define fog computing as an "end-to-end horizontal architecture" for the IoT that distributes the compute and storage, control and communication planes nearer to users "along the cloud-to-thing continuum". Aazam and Huh [1] describe specifically how this vision can be realised in terms of additional security, storage, processing and monitoring sub-layers between the physical layer and the transport layer of an IoT architecture.…”

Section: Data Collection: Discovery Management Transmission Contexmentioning

confidence: 99%

Analytics for the Internet of Things

2018

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The Internet of Things (IoT) envisions a world-wide, interconnected network of smart physical entities. These physical entities generate a large amount of data in operation and as the IoT gains momentum in terms of deployment, the combined scale of those data seems destined to continue to grow. Increasingly, applications for the IoT involve analytics. Data analytics is the process of deriving knowledge from data, generating value like actionable insights from them. This article reviews work in the IoT and big data analytics from the perspective of their utility in creating efficient, effective and innovative applications and services for a wide spectrum of domains. We review the broad vision for the IoT as it is shaped in various communities, examine the application of data analytics across IoT domains, provide a categorisation of analytic approaches and propose a layered taxonomy from IoT data to analytics. This taxonomy provides us with insights on the appropriateness of analytical techniques, which in turn shapes a survey of enabling technology and infrastructure for IoT analytics. Finally, we look at some tradeoffs for analytics in the IoT that can shape future research.

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Clarifying Fog Computing and Networking: 10 Questions and Answers

Cited by 209 publications

References 0 publications

Online Edge Caching and Wireless Delivery in Fog-Aided Networks With Dynamic Content Popularity

Online Edge Caching and Wireless Delivery in Fog-Aided Networks With Dynamic Content Popularity

A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges

Analytics for the Internet of Things

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