Pareto Based Virtual Machine Selection with Load Balancing in Cloud Data Centre

Naik, Ketaki; Gandhi, G. Meera; Patil, Suhas H.

doi:10.2478/cait-2018-0036

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…The main aim of this approach is to predict the future CPU usage of demand traces. In [20] a model is proposed, based on improved genetic algorithm to improve virtual machine selection performance while achieving the load balancing in the cloud data center. Authors propose a prediction algorithm to predict the future usage of resources based on the backpropagation neural networks in [21].…”

Section: Related Workmentioning

confidence: 99%

HPCWMF: A Hybrid Predictive Cloud Workload Management Framework Using Improved LSTM Neural Network

Kumar

Umamaheswari

2020

Cybernetics and Information Technologies

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For cloud providers, workload prediction is a challenging task due to irregular incoming workloads from users. Accurate workload prediction is essential for scheduling the resources to the cloud applications. Thus, in this paper, the authors propose a predictive cloud workload management framework to estimate the needed resources in advance based on a hybrid approach, which is a combination of an improved Long Short-Term Memory (LSTM) network and a multilayer perceptron network. By improving the traditional LSTM architecture by using opposition-based differential evolution algorithm and dropout technique on recurrent connection without memory loss, the proposed approach has the ability to perform a better prediction process. A novel hybrid predictive approach is aiming at enhancing the prediction performance of the cloud workload. Finally, the authors measure the proposed approach’s effectiveness under benchmark data sets of NASA and Saskatchewan servers. The experimental results proved that the proposed approach outperforms the other conventional methods.

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Section: Related Workmentioning

confidence: 99%

HPCWMF: A Hybrid Predictive Cloud Workload Management Framework Using Improved LSTM Neural Network

Kumar

Umamaheswari

2020

Cybernetics and Information Technologies

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“…The approach of load balancing among the virtual machines in the cloud data center using the Pareto principle is mentioned in [31]. As the computation requirement of the applications keeps varying, there is a necessity to scale up and scale down the virtual machines so Pareto based genetic algorithm is used to generate a large number of solutions and then select one of the solutions as the best one.…”

Section: Related Workmentioning

confidence: 99%

Uncertainty Aware Resource Provisioning Framework for Cloud Using Expected 3-SARSA Learning Agent: NSS and FNSS Based Approach

Bhargavi

Babu²

2019

Cybernetics and Information Technologies

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Efficiently provisioning the resources in a large computing domain like cloud is challenging due to uncertainty in resource demands and computation ability of the cloud resources. Inefficient provisioning of the resources leads to several issues in terms of the drop in Quality of Service (QoS), violation of Service Level Agreement (SLA), over-provisioning of resources, under-provisioning of resources and so on. The main objective of the paper is to formulate optimal resource provisioning policies by efficiently handling the uncertainties in the jobs and resources with the application of Neutrosophic Soft-Set (NSS) and Fuzzy Neutrosophic Soft-Set (FNSS). The performance of the proposed work compared to the existing fuzzy auto scaling work achieves the throughput of 80% with the learning rate of 75% on homogeneous and heterogeneous workloads by considering the RUBiS, RUBBoS, and Olio benchmark applications.

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