Resource Provisioning for IoT Services in the Fog

Skarlat, Olena; Schulte, Stefan; Borkowski, Michael; Leitner, Philipp

doi:10.1109/soca.2016.10

Cited by 166 publications

(97 citation statements)

References 18 publications

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“…In a first set, we present the articles that included both communication and node execution time. Skarlat et al obtained a decrease of the network delay and the execution time up to 39% with regard to a baseline policy. Zeng et al also considered both network and execution times.…”

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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“…This paper extends our previous work [30] (i) by providing a motivational scenario based on the application of fog computing for Cyber-Physical Systems (CPS), (ii) by adding a formal model of the fog landscape and IoT applications to be executed in that landscape, (iii) by providing a formal definition of the Fog Service Placement Problem (FSPP), and (iv) by implementing heuristics to solve the FSPP. With respect to [30], we have also replaced the simulation environment in favor of iFogSim [17].…”

Section: Introductionmentioning

confidence: 84%

“…All three service types, i.e., sensing, processing, and actuating services, can be executed in the fog colony and the cloud, whereas only processing services can be propagated to the closest neighbor colony. The processing cost in the cloud is $0.30 per billing time unit (BTU), i.e., 1 h, as in [30].…”

Section: Methodsmentioning

confidence: 99%

“…In the First Fit scenario, a service placement plan is produced by searching a first fit fog resource [6,30]. Hence, a list of all available fog devices in the fog landscape is sorted by the communication link delays between each resource and the fog orchestration control node, by available resource capacities, and by the types of services.…”

Section: Evaluation Scenariosmentioning

confidence: 99%

“…Our former works [29,30] consider both the structure of a fog landscape and service placement mechanisms. The paper at hand extends those contributions offering additional service placement mechanisms (i.e., the genetic algorithmbased heuristic) and evaluation experiments.…”

Section: Related Workmentioning

confidence: 99%

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Optimized IoT service placement in the fog

Skarlat

Nardelli

Schulte

et al. 2017

SOCA

Self Cite

307

183

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The Internet of Things (IoT) leads to an evergrowing presence of ubiquitous networked computing devices in public, business, and private spaces. These devices do not simply act as sensors, but feature computational, storage, and networking resources. Being located at the edge of the network, these resources can be exploited to execute IoT applications in a distributed manner. This concept is known as fog computing. While the theoretical foundations of fog computing are already established, there is a lack of resource provisioning approaches to enable the exploitation of fogbased computational resources. To resolve this shortcoming, we present a conceptual fog computing framework. Then, we model the service placement problem for IoT applications over fog resources as an optimization problem, which explicitly considers the heterogeneity of applications and resources in terms of Quality of Service attributes. Finally, we propose a genetic algorithm as a problem resolution heuristic and show, through experiments, that the service execution can achieve a reduction of network communication delays when the genetic algorithm is used, and a better utilization of fog resources when the exact optimization method is applied.

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Elucidating the challenges for the praxis of fog computing: An aspect‐based study

Martin

Kandasamy

Chandrasekaran

et al. 2019

Int J Communication

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Summary The evolutionary advancements in the field of technology have led to the instigation of cloud computing. The Internet of Things paradigm stimulated the extensive use of sensors distributed across the network edges. The cloud datacenters are assigned the responsibility for processing the collected sensor data. Recently, fog computing was conceptuated as a solution for the overwhelmed narrow bandwidth. The fog acts as a complementary layer that interplays with the cloud and edge computing layers, for processing the data streams. The fog paradigm, as any distributed paradigm, has its set of inherent challenges. The fog environment necessitates the development of management platforms that effectuates the orchestration of fog entities. Owing to the plenitude of research efforts directed toward these issues in a relatively young field, there is a need to organize the different research works. In this study, we provide a compendious review of the research approaches in the domain, with special emphasis on the approaches for orchestration and propose a multilevel taxonomy to classify the existing research. The study also highlights the application realms of fog computing and delineates the open research challenges in the domain.

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Resource Provisioning for IoT Services in the Fog

Cited by 166 publications

References 18 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

Optimized IoT service placement in the fog

Elucidating the challenges for the praxis of fog computing: An aspect‐based study

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