New Crossover Operator of Genetic Algorithms for the TSP

Su, Fanchen; Zhu, Fang; Yin, Zhiyi; Yao, Haitao; Wang, Qingping; Dong, Wei

doi:10.1109/cso.2009.422

Cited by 7 publications

(2 citation statements)

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“…12 Al-khafajiy et al suggested new architecture for the fog computing to improve the QoS by offloading of request model; this model uses collaboration strategy for processing of data in the shared medium which helps in serving a large number of IoT requests and improves the QoS, and from the obtained result, it is clear that performance of fog nodes increases when the load has been distributed. 13 Raman et al compare the existing resource allocation algorithms and scheduling algorithms and categorize them based on their type, cost, energy, and time. 14 Iyapparaja M et al suggested an algorithm to duplicate the early finish time so we can schedule mark task for data center in heterogeneous network.…”

Section: Related Workmentioning

confidence: 99%

Improving quality‐of‐service in fog computing through efficient resource allocation

Mani

Iyapparaja

2020

Computational Intelligence

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In today's world, large group migration of applications to the fog computing is registered in the information technology world. The main issue in fog computing is providing enhanced quality of service (QoS). QoS management consists of various method used for allocating fog-user applications in the virtual environment and selecting suitable method for allocating virtual resources to physical resource. The resources allocation in effective manner in the fog environment is also a major problem in fog computing; it occurs when the infrastructure is build using lightweight computing devices. In this article, the allocation of task and placement of virtual machine problems is explained in the single fog computing environment. The experiment is done and the result shows that the proposed framework improves QoS in fog environment.

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Section: Related Workmentioning

confidence: 99%

Improving quality‐of‐service in fog computing through efficient resource allocation

Mani

Iyapparaja

2020

Computational Intelligence

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“…The fitness function is the total cost of the VM placement represented by each chromosome. The lower the total cost, the fitter the VM placement represented by that chromosome [30]. Thus, the chromosomes with lower values are selected for the generation of the next population.…”

Section: Virtual Machine Placement Algorithmic Solutionmentioning

confidence: 99%

Improving Quality-of-Service in Cloud/Fog Computing through Efficient Resource Allocation

Akintoye

Bagula

2019

Sensors

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Recently, a massive migration of enterprise applications to the cloud has been recorded in the IT world. One of the challenges of cloud computing is Quality-of-Service management, which includes the adoption of appropriate methods for allocating cloud-user applications to virtual resources, and virtual resources to the physical resources. The effective allocation of resources in cloud data centers is also one of the vital optimization problems in cloud computing, particularly when the cloud service infrastructures are built by lightweight computing devices. In this paper, we formulate and present the task allocation and virtual machine placement problems in a single cloud/fog computing environment, and propose a task allocation algorithmic solution and a Genetic Algorithm Based Virtual Machine Placement as solutions for the task allocation and virtual machine placement problem models. Finally, the experiments are carried out and the results show that the proposed solutions improve Quality-of-Service in the cloud/fog computing environment in terms of the allocation cost.

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Comparative Study on Different Encoding Strategies for Multiple Traveling Salesmen Problem

Dou,

Yang,

et al. 2023

2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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New Crossover Operator of Genetic Algorithms for the TSP

Cited by 7 publications

References 0 publications

Improving quality‐of‐service in fog computing through efficient resource allocation

Improving quality‐of‐service in fog computing through efficient resource allocation

Improving Quality-of-Service in Cloud/Fog Computing through Efficient Resource Allocation

Comparative Study on Different Encoding Strategies for Multiple Traveling Salesmen Problem

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