Improved FIFO Scheduling Algorithm Based on Fuzzy Clustering in Cloud Computing

Li, Jian; Ma, Tinghuai; Tang, Meili; Shen, Wenhai; Jin, Yuanfeng

doi:10.3390/info8010025

Cited by 26 publications

(17 citation statements)

References 11 publications

(15 reference statements)

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“…So, traditional techniques are not feasible in cloud environment scheduling [66]. Many works have been carried out to improve the implementation of the traditional techniques [63,[67][68][69][70]. Round robin is one of these techniques that work using time slice or a quantum.…”

Section: Task Scheduling Techniquesmentioning

confidence: 99%

Task scheduling in cloud computing based on metaheuristic techniques: A review paper

Al-Arasi¹,

Saif²

2020

EAI Endorsed Transactions on Cloud Systems

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Cloud computing delivers computing resources like software and hardware as a service to the users through a network. Due to the scale of the modern datacentres and their dynamic resources provisioning nature, we need efficient scheduling techniques to manage these resources. The main objective of scheduling is to assign tasks to adequate resources in order to achieve one or more optimization criteria. Scheduling is a challenging issue in the cloud environment, therefore many researchers have attempted to explore an optimal solution for task scheduling in the cloud environment. They have shown that traditional scheduling is not efficient in solving this problem and produce an optimal solution with polynomial time in the cloud environment. However, they introduced sub-optimal solutions within a short period of time. Meta-heuristic techniques have provided near-optimal or optimal solutions within an acceptable time for such problems. In this work, we have introduced the major concepts of resource scheduling and provided a comparative analysis of many task scheduling techniques based on different optimization criteria.

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Section: Task Scheduling Techniquesmentioning

confidence: 99%

Task scheduling in cloud computing based on metaheuristic techniques: A review paper

Al-Arasi¹,

Saif²

2020

EAI Endorsed Transactions on Cloud Systems

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“…There are several other optimisation algorithms for CM including artificial neural network (Maqableh et al, 2014;Pooranian et al, 2014;Heckerling et al, 2004), fuzzy logic (Abd et al, 2013;Li and Zhang, 2012;Zhang et al, 2014), particle swarm optimisation (PSO) (Pandey et al, 2010;Netjinda et al, 2012;Guo et al, 2012), bee colony optimisation (Bitam, 2012;Mizan et al, 2012), bat algorithm Malakooti et al, 2013).…”

Section: Variations Of Gamentioning

confidence: 99%

A survey on cloud-based solutions for cloud manufacturing

Brintha

Benedict

2018

IJCAET

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Cloud computing is becoming an emerging technology that is changing the traditional industry and enterprises to do their work in a new way using dynamically scalable, virtualised heterogeneous resources over the internet. Nowadays, cloud manufacturing is evolving as a new technology that transforms the traditional manufacturing business model to effective reconfigurable production lines, allows effective load balancing to dynamic customer demands and reduces the time to market. In cloud manufacturing, the resources that are geographically distributed across several sectors are incorporated as cloud-based services and they are provided to users in a centralised manner. The resources can be provisioned to the users of cloud as pay as you go model depending on the user requirements.

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“…Zhang et al [18] proposed a high-order possibilistic c-means algorithm (HOPCM) for big data clustering by optimizing the objective function in the tensor space. Li et al [19] proposed a task scheduling algorithm based on fuzzy clustering algorithms. However, there are still some problems, such as long convergence time.…”

Section: Introductionmentioning

confidence: 99%

Modified Immune Evolutionary Algorithm for Medical Data Clustering and Feature Extraction under Cloud Computing Environment

Liu

2020

Journal of Healthcare Engineering

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Medical data have the characteristics of particularity and complexity. Big data clustering plays a significant role in the area of medicine. The traditional clustering algorithms are easily falling into local extreme value. It will generate clustering deviation, and the clustering effect is poor. Therefore, we propose a new medical big data clustering algorithm based on the modified immune evolutionary method under cloud computing environment to overcome the above disadvantages in this paper. Firstly, we analyze the big data structure model under cloud computing environment. Secondly, we give the detailed modified immune evolutionary method to cluster medical data including encoding, constructing fitness function, and selecting genetic operators. Finally, the experiments show that this new approach can improve the accuracy of data classification, reduce the error rate, and improve the performance of data mining and feature extraction for medical data clustering.

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Improved FIFO Scheduling Algorithm Based on Fuzzy Clustering in Cloud Computing

Cited by 26 publications

References 11 publications

Task scheduling in cloud computing based on metaheuristic techniques: A review paper

Task scheduling in cloud computing based on metaheuristic techniques: A review paper

A survey on cloud-based solutions for cloud manufacturing

Modified Immune Evolutionary Algorithm for Medical Data Clustering and Feature Extraction under Cloud Computing Environment

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