QoS-Aware Deployment of IoT Applications Through the Fog

Brogi, Antonio; Forti, Stefano

doi:10.1109/jiot.2017.2701408

Cited by 375 publications

(255 citation statements)

References 26 publications

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“…Significantly inspired by Brogi and Forti, Xia et al proposed a backtracking solution to FAPP to minimize the average response time of deployed IoT applications. Two new heuristics were devised.…”

Section: Analysis Of the State Of The Artmentioning

confidence: 99%

How to place your apps in the fog: State of the art and open challenges

et al. 2019

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Summary Fog computing aims at extending the cloud towards the Internet of things so to achieve improved quality of service and to empower latency‐sensitive and bandwidth‐hungry applications. The fog calls for novel models and algorithms to distribute multiservice applications in such a way that data processing occurs wherever it is best placed, based on both functional and nonfunctional requirements. This survey reviews the existing methodologies to solve the application placement problem in the fog, while pursuing three main objectives. First, it offers a comprehensive overview on the currently employed algorithms, on the availability of open‐source prototypes and on the size of test use cases. Second, it classifies the literature based on the application and fog infrastructure characteristics that are captured by available models, with a focus on the considered constraints and the optimized metrics. Finally, it identifies some open challenges in application placement in the fog.

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Section: Analysis Of the State Of The Artmentioning

confidence: 99%

How to place your apps in the fog: State of the art and open challenges

et al. 2019

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“…We have already introduced these resource classes, and now, we contrast their resource and performance characteristics. We base these on existing definitions, many of which place fog computing as a resource layer that fits between the edge devices and the cloud data centers, with features that resemble both.…”

Section: The Taxonomymentioning

confidence: 99%

“…These have also been extended to include cost/time budgets/goals using functions like GAIN and LOSS . There are greedy algorithms for scheduling BoTs on cloud resources and DAG scheduling on edge devices . They use heuristics such as predicting the proximity between mobile edge devices for deadline planning, prioritizing tasks with the most resource constraints, and incrementally colocating tasks on the same resource to avoid network latency.…”

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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“…Fog computing stresses the proximity to terminal users and client objectives, dense geographical distribution and local resource pools, latency reduction, and backbone bandwidth savings to achieve better quality of service (QoS) [18].…”

Section: Introductionmentioning

confidence: 99%

Processing Optimization of Typed Resources with Synchronized Storage and Computation Adaptation in Fog Computing

Song

Duan

Wan

et al. 2018

Wireless Communications and Mobile Computing

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Wide application of the Internet of Things (IoT) system has been increasingly demanding more hardware facilities for processing various resources including data, information, and knowledge. With the rapid growth of generated resource quantity, it is difficult to adapt to this situation by using traditional cloud computing models. Fog computing enables storage and computing services to perform at the edge of the network to extend cloud computing. However, there are some problems such as restricted computation, limited storage, and expensive network bandwidth in Fog computing applications. It is a challenge to balance the distribution of network resources. We propose a processing optimization mechanism of typed resources with synchronized storage and computation adaptation in Fog computing. In this mechanism, we process typed resources in a wireless-network-based threetier architecture consisting of Data Graph, Information Graph, and Knowledge Graph. The proposed mechanism aims to minimize processing cost over network, computation, and storage while maximizing the performance of processing in a business value driven manner. Simulation results show that the proposed approach improves the ratio of performance over user investment. Meanwhile, conversions between resource types deliver support for dynamically allocating network resources.

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QoS-Aware Deployment of IoT Applications Through the Fog

Cited by 375 publications

References 26 publications

How to place your apps in the fog: State of the art and open challenges

How to place your apps in the fog: State of the art and open challenges

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

Processing Optimization of Typed Resources with Synchronized Storage and Computation Adaptation in Fog Computing

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