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An Automated Task Scheduling Model using Non-Dominated Sorting Genetic Algorithm II for Fog-Cloud Systems
Faculty
Computer Science
Year:
2020
Type of Publication:
ZU Hosted
Pages:
Authors:
Karam mohamed goda
Staff Zu Site
Abstract In Staff Site
Journal:
IEEE Transactions on Cloud Computing IEEE xplore
Volume:
Keywords :
, Automated Task Scheduling Model using Non-Dominated
Abstract:
In this paper, we first propose a multi-objective task-scheduling optimization problem that minimizes both the makespans and total costs in a fog-cloud environment. Then, we suggest an optimization model based on a Discrete Non-dominated Sorting Genetic Algorithm II (DNSGA-II) to deal with the discrete multi-objective task-scheduling problem and to automatically allocate tasks that should be executed either on fog or cloud nodes. The NSGA-II algorithm is adapted to discretize crossover and mutation evolutionary operators, rather than using continuous operators that require high computational resources and not able to allocate proper computing nodes. In our model, the communications between the fog and cloud tiers are formulated as a multi-objective function to optimize the execution of tasks. The proposed model allocates computing resources that would effectively run on either the fog or cloud nodes. Moreover, it efficiently organizes the distribution of workloads through various computing resources at the fog. Several experiments are conducted to determine the performance of the proposed model compared with a continuous NSGA-II (CNSGA-II) algorithm and four peer mechanisms. The outcomes demonstrate that the model is capable of achieving dynamic task scheduling with minimizing the total execution times (i.e. makespans) and costs in fog-cloud environments
Author Related Publications
Karam mohamed goda, "An efficient teaching-learning-based optimization algorithm for parameters identification of photovoltaic models: Analysis and validations", Pergamon, 2021
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Karam mohamed goda, "An Improved Binary Grey-Wolf Optimizer With Simulated Annealing for Feature Selection", IEEE, 2021
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Karam mohamed goda, "Evolutionary algorithm-based convolutional neural network for predicting heart diseases", Elsevier, 2021
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Karam mohamed goda, "An improved gaining-sharing knowledge algorithm for parameter extraction of photovoltaic models", Elsevier, 2021
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Department Related Publications
Mohammed Abdel Basset Metwally Attia, "Discrete greedy flower pollination algorithm for spherical traveling salesman problem", Springer, 2019
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Mohammed Abdel Basset Metwally Attia, "A New Hybrid Flower Pollination Algorithm for Solving Constrained Global Optimization Problems", Natural Sciences Publishing Cor., 2014
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Saber Mohamed, "Training and Testing a Self-Adaptive Multi-Operator Evolutionary Algorithm for Constrained Optimization", ELSEVEIR, 2015
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Saber Mohamed, "An Improved Self-Adaptive Differential Evolution Algorithm for Optimization Problems", IEEE, 2013
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Saber Mohamed, "Differential Evolution with Dynamic Parameters Selection for Optimization Problems", IEEE, 2014
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