2020
DOI: 10.1007/s10723-020-09507-1
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Workload Allocation in IoT-Fog-Cloud Architecture Using a Multi-Objective Genetic Algorithm

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Cited by 71 publications
(36 citation statements)
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“…Feedback delay, 4.V2V propagation delay. Where, the upload delay of i task can be expressed as Equation (7).…”
Section: Time Consumption Modelmentioning
confidence: 99%
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“…Feedback delay, 4.V2V propagation delay. Where, the upload delay of i task can be expressed as Equation (7).…”
Section: Time Consumption Modelmentioning
confidence: 99%
“…Literature [7] solves the problem of workload distribution in the Internet of Things -fog -cloud architecture, and makes a tradeoff between energy consumption and propagation delay. NSGA-II algorithm is used to process the multi-objective model, and simulation calculation is carried out in three scenarios of fog, pure cloud and fog cloud, which proves that this scheme has significant differences.…”
Section: Introductionmentioning
confidence: 99%
“…The selection of optimal nodes at each step leads to global optimization of reliable routing in healthcare systems on the IoT platform. The application of multiobjective genetic optimization algorithms has been proven in many fields of research, including the energy-efficient routing in wireless sensor networks [15], resource allocation in the cloud-fog-IoT infrastructure [16], service placement, and load distribution in edge computing [17]. In general, the contribution of this paper can be summarized in four steps:…”
Section: Introductionmentioning
confidence: 99%
“…Literature [12] proposes a new cross-task knowledge transfer mode based on denoising autoencoder for single-objective and multi-objective multi-task optimization. Literature [13] balances the relationship between energy consumption and time delay when the fog and cloud process the workload, and adopts NSGAII algorithm for effective processing.…”
Section: Introductionmentioning
confidence: 99%
“…Literature [7] conducts a targeted study on the dependency relationship among various tasks, proposes a offloading strategy combining workflow scheduling, and uses genetic algorithm to optimize it. Literature [8] solves the problem of workload distribution in the fog cloud architecture of the Internet of Things. NSGAII algorithm is used as the optimization method to reduce the time delay and meet the energy consumption requirements under the three-layer architecture of the Internet of Things, fog and cloud.…”
Section: Introductionmentioning
confidence: 99%