Demystifying Fog Computing: Characterizing Architectures, Applications and Abstractions

Varshney, Prateeksha; Simmhan, Yogesh

doi:10.1109/icfec.2017.20

Cited by 144 publications

(157 citation statements)

References 31 publications

(95 reference statements)

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“…Physical presence, access limits, and mobility of cloud, fog, and edge resources . A, Physical presence versus access; B, Physical mobility.…”

Section: The Taxonomymentioning

confidence: 99%

“…Resource characteristics of cloud, fog, and edge computing systems [Colour figure can be viewed at wileyonlinelibrary.com]…”

Section: The Taxonomymentioning

confidence: 99%

“…Heterogeneity is increased when the resources are consumer‐owned instead of being available as part of commercial deployment, such as smartphones, smart watches, virtual reality headsets, etc. Edge platforms also tend to be constrained devices, with battery or memory capacity often being the limiting factor rather than even compute capability . The fog layer offers compute resources with a higher capacity than the edge but to a smaller scale than clouds .…”

Section: The Taxonomymentioning

confidence: 99%

“…The latter allow fogs to serve as a “reverse content delivery network” to let edge devices stage data on them and eventually push them to the cloud for archival, after some preprocessing . Resources may also have variable network characteristics . While the fog is close to the edge in the network topology, whether it is at a one‐hop or multihop depends on the deployment .…”

Section: The Taxonomymentioning

confidence: 99%

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Characterizing application scheduling on edge, fog, and cloud computing resources

Varshney

Simmhan

2019

Softw Pract Exp

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Summary Cloud computing has grown to become a popular distributed computing service offered by commercial providers. More recently, edge and fog computing resources have emerged on the wide‐area network as part of Internet of things (IoT) deployments. These three resource abstraction layers are complementary, and offer distinctive benefits. Scheduling applications on clouds has been an active area of research, with workflow and data flow models offering a flexible abstraction to specify applications for execution. However, the application programming and scheduling models for edge and fog are still maturing, and can benefit from learnings on cloud resources. At the same time, there is also value in using these resources cohesively for application execution. In this article, we offer a taxonomy of concepts essential for specifying and solving the problem of scheduling applications on edge, fog, and cloud computing resources. We first characterize the resource capabilities and limitations of these infrastructure and offer a taxonomy of application models, quality‐of‐service constraints and goals, and scheduling techniques, based on a literature review. We also tabulate key research prototypes and papers using this taxonomy. This survey benefits developers and researchers on these distributed resources in designing and categorizing their applications, selecting the relevant computing abstraction(s), and developing or selecting the appropriate scheduling algorithm. It also highlights gaps in literature where open problems remain.

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“…Physical presence, access limits, and mobility of cloud, fog, and edge resources . A, Physical presence versus access; B, Physical mobility.…”

Section: The Taxonomymentioning

confidence: 99%

“…Resource characteristics of cloud, fog, and edge computing systems [Colour figure can be viewed at wileyonlinelibrary.com]…”

Section: The Taxonomymentioning

confidence: 99%

Section: The Taxonomymentioning

confidence: 99%

Section: The Taxonomymentioning

confidence: 99%

See 2 more Smart Citations

Characterizing application scheduling on edge, fog, and cloud computing resources

Varshney

Simmhan

2019

Softw Pract Exp

Self Cite

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“…Another significant difference regarding the Cloud is that the Fog encompasses a huge set of heterogeneous devices, mostly wirelessly connected, that are more constrained in terms of resources in comparison with the servers that reside in the Cloud [29].…”

Section: From Cloud To Fog Computingmentioning

confidence: 99%

Fog orchestration for the Internet of Everything: state-of-the-art and research challenges

Velásquez

Abreu

Assis

et al. 2018

J Internet Serv Appl

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Recent developments in telecommunications have allowed drawing new paradigms, including the Internet of Everything, to provide services by the interconnection of different physical devices enabling the exchange of data to enrich and automate people's daily activities; and Fog computing, which is an extension of the well-known Cloud computing, bringing tasks to the edge of the network exploiting characteristics such as lower latency, mobility support, and location awareness. Combining these paradigms opens a new set of possibilities for innovative services and applications; however, it also brings a new complex scenario that must be efficiently managed to properly fulfill the needs of the users. In this scenario, the Fog Orchestrator component is the key to coordinate the services in the middle of Cloud computing and Internet of Everything. In this paper, key challenges in the development of the Fog Orchestrator to support the Internet of Everything are identified, including how they affect the tasks that a Fog service Orchestrator should perform. Furthermore, different service Orchestrator architectures for the Fog are explored and analyzed in order to identify how the previously listed challenges are being tackled. Finally, a discussion about the open challenges, technological directions, and future of the research on this subject is presented.

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RIoTBench: An IoT benchmark for distributed stream processing systems

Shukla

Chaturvedi

Simmhan

2017

Concurrency and Computation

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134

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The Internet of Things (IoT) is an emerging technology paradigm where millions of sensors and actuators help monitor and manage, physical, environmental and human systems in real-time. The inherent closedloop responsiveness and decision making of IoT applications make them ideal candidates for using low latency and scalable stream processing platforms. Distributed Stream Processing Systems (DSPS) hosted on Cloud data-centers are becoming the vital engine for real-time data processing and analytics in any IoT software architecture. But the efficacy and performance of contemporary DSPS have not been rigorously studied for IoT applications and data streams. Here, we develop RIoTBench, a Realtime IoT Benchmark suite, along with performance metrics, to evaluate DSPS for streaming IoT applications. The benchmark includes 27 common IoT tasks classified across various functional categories and implemented as reusable micro-benchmarks. Further, we propose four IoT application benchmarks composed from these tasks, and that leverage various dataflow semantics of DSPS. The applications are based on common IoT patterns for data pre-processing, statistical summarization and predictive analytics. These are coupled with four stream workloads sourced from real IoT observations on smart cities and fitness, with peak streams rates that range from 500 − 10, 000 messages/sec and diverse frequency distributions. We validate the RIoTBench suite for the popular Apache Storm DSPS on the Microsoft Azure public Cloud, and present empirical observations. This suite can be used by DSPS researchers for performance analysis and resource scheduling, and by IoT practitioners to evaluate DSPS platforms. arXiv:1701.08530v1 [cs.DC] 30 Jan 2017 1. We classify different characteristics of streaming applications, their composition semantics, and their data sources, in § 3.2. Then, in § 4, we propose categories of tasks that are essential for IoT applications and the key features of input data streams they operate upon.3. We identify performance metrics of DSPS that are necessary to meet the latency and scalability needs of streaming IoT applications, in § 5.4. We propose the RIoTBench real-time IoT benchmark for DSPS based on representative micro-benchmark tasks, drawn from the above categories, in § 6. We design four reference IoT applications that span Data preprocessing, Statistical analytics and Predictive Analytics, and are composed from these tasks. We also identify four real-world streams with different distributions as workloads on which to evaluate them.5. Lastly, we validate the proposed benchmark suite for the popular Apache Storm DSPS, and present empirical results for the same in § 7.Our contributions benefit two classes of audience. One, for developers and users in IoT domains, RIoTBench offers a set of realistic IoT tasks and applications that they can customize and configure to help evaluate candidate DSPS platforms for their performance and scalability needs. Two, for researchers on Big Data, it provides a reference micro and applicat...

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Demystifying Fog Computing: Characterizing Architectures, Applications and Abstractions

Cited by 144 publications

References 31 publications

Characterizing application scheduling on edge, fog, and cloud computing resources

Characterizing application scheduling on edge, fog, and cloud computing resources

Fog orchestration for the Internet of Everything: state-of-the-art and research challenges

RIoTBench: An IoT benchmark for distributed stream processing systems

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