Resolving classical concurrency problems using adaptive conflictless scheduling

Smoliński, Mateusz

doi:10.1109/inista.2017.8001193

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…Regardless, Figure 5 exposes the overall picture. The equations describing the types of entities involved are Equation (22) for end devices (H), which accounts for more items than Equation ( 16), Equation (23) for edge nodes (F), whose difference with Equation ( 17) is that now an edge has k downlink ports, going from 0 to k − 1, and 1 uplink port identified by k, and also Equation (24) for fog devices (F), which is analogous to Equation (18).…”

Section: Extended Scenario: Multipleenddevice-edge-fogmentioning

confidence: 99%

“…Generally speaking, FDT are mainly focused on studying distributed systems being run in a non deterministic fashion so as to check for abnormal conditions, leading to malfunction and degradation in system performance, thus posing a risk to system integrity. The most popular of those conditions is deadlock, which may be provoked by different circumstances such as mutual exclusion or circular wait, whereas other conditions may arise, such as livelock, data race or resource starvation [23].…”

Section: Introductionmentioning

confidence: 99%

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Modeling an Edge Computing Arithmetic Framework for IoT Environments

Roig

Alcaraz

Gilly

et al. 2022

Sensors

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IoT environments are forecasted to grow exponentially in the coming years thanks to the recent advances in both edge computing and artificial intelligence. In this paper, a model of remote computing scheme is presented, where three layers of computing nodes are put in place in order to optimize the computing and forwarding tasks. In this sense, a generic layout has been designed so as to easily achieve communications among the diverse layers by means of simple arithmetic operations, which may result in saving resources in all nodes involved. Traffic forwarding is undertaken by means of forwarding tables within network devices, which need to be searched upon in order to find the proper destination, and that process may be resource-consuming as the number of entries in such tables grow. However, the arithmetic framework proposed may speed up the traffic forwarding decisions as relaying on integer divisions and modular arithmetic, which may result more straightforward. Furthermore, two diverse approaches have been proposed to formally describe such a design by means of coding with Spin/Promela, or otherwise, by using an algebraic approach with Algebra of Communicating Processes (ACP), resulting in a explosion state for the former and a specified and verified model in the latter.

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Section: Extended Scenario: Multipleenddevice-edge-fogmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling an Edge Computing Arithmetic Framework for IoT Environments

Roig

Alcaraz

Gilly

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Here, the ACP model of the entities are described, such as PUB i in Equation ( 9), EDGE m in Equation (10), FOG in Equation (11), CLOUD in Equation ( 12), and SUB j in Equation (13). In this framework, it is to be said that Equation ( 9) is analogous to Equation (1), as well as Equations (10) to Equation ( 2), and Equations (13) to Equation (4).…”

Section: Fog Scenariomentioning

confidence: 99%

Modeling of a Generic Edge Computing Application Design

Roig

Alcaraz

Gilly

et al. 2021

Sensors

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Edge computing applications leverage advances in edge computing along with the latest trends of convolutional neural networks in order to achieve ultra-low latency, high-speed processing, low-power consumptions scenarios, which are necessary for deploying real-time Internet of Things deployments efficiently. As the importance of such scenarios is growing by the day, we propose to undertake two different kind of models, such as an algebraic models, with a process algebra called ACP and a coding model with a modeling language called Promela. Both approaches have been used to build models considering an edge infrastructure with a cloud backup, which has been further extended with the addition of extra fog nodes, and after having applied the proper verification techniques, they have all been duly verified. Specifically, a generic edge computing design has been specified in an algebraic manner with ACP, being followed by its corresponding algebraic verification, whereas it has also been specified by means of Promela code, which has been verified by means of the model checker Spin.

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Outlier Detection Using the Multiobjective Genetic Algorithm

Duraj

Chomatek

2020

Journal of Applied Computer Science

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Resolving classical concurrency problems using adaptive conflictless scheduling

Cited by 4 publications

References 9 publications

Modeling an Edge Computing Arithmetic Framework for IoT Environments

Modeling an Edge Computing Arithmetic Framework for IoT Environments

Modeling of a Generic Edge Computing Application Design

Outlier Detection Using the Multiobjective Genetic Algorithm

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