A Cloud-based Conceptual Framework for Multi-Objective Virtual Machine Scheduling using Whale Optimization Algorithm

Rana, Nadim; Latiff, Muhammad Shafie Abd; Abdulhamid, Shafií Muhammad

doi:10.11113/ijic.v8n3.199

Cited by 14 publications

(7 citation statements)

References 10 publications

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“…This method is suitable for homogeneous VMP because the bit value of velocity vector depends on presence or absence of VMs only and do not perfectly encode the number and type of VMs, hence not suitable for heterogeneous environment. In addition, energy and resource efficient VM allocations based on whale optimization and its hybrid approaches are presented in [37], [38]. Basset et al proposed a bandwidth-efficient VMP in [39] by applying improved levy based WOA hybridized with best-fit strategy.…”

Section: Related Workmentioning

confidence: 99%

A Secure and Multi-objective Virtual Machine Placement Framework for Cloud Data Centre

Saxena,

Gupta,

Kumar

et al. 2021

Preprint

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To facilitate cost-effective and elastic computing benefits to the cloud users, the energy-efficient and secure allocation of virtual machines (VMs) plays a significant role at the data centre. The inefficient VM Placement (VMP) and sharing of common physical machines among multiple users leads to resource wastage, excessive power consumption, increased inter-communication cost and security breaches. To address the aforementioned challenges, a novel secure and multi-objective virtual machine placement (SM-VMP) framework is proposed with an efficient VM migration. The proposed framework ensures an energy-efficient distribution of physical resources among VMs that emphasizes secure and timely execution of user application by reducing inter-communication delay. The VMP is carried out by applying the proposed Whale Optimization Genetic Algorithm (WOGA), inspired by whale evolutionary optimization and nondominated sorting based genetic algorithms. The performance evaluation for static and dynamic VMP and comparison with recent state-of-the-arts observed a notable reduction in shared servers, inter-communication cost, power consumption and execution time up to 28.81%, 25.7%, 35.9% and 82.21%, respectively and increased resource utilization up to 30.21%.

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Section: Related Workmentioning

confidence: 99%

A Secure and Multi-objective Virtual Machine Placement Framework for Cloud Data Centre

Saxena,

Gupta,

Kumar

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…WOA is also used in the cloud computing research environment. A conceptual framework is proposed by [26] to solve scheduling problems of multi-objective virtual machines. They used WOA and presented a problem formulation for the framework to achieve multi-objective functions.…”

Section: Related Workmentioning

confidence: 99%

An Author Gender Detection Method Using Whale Optimization Algorithm and Artificial Neural Network

et al. 2020

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Author gender detection (AGD) is a serious and crucial issue in Internet security applications, in particular in email, messenger, and social network communications. Detecting the gender of communication partner helps preventing massive fraud and abuses happening through social media such as email, blogs, forums. Text and writings of people on the Internet have valuable information that can be used to identify the gender of an author. Machine learning and meta-heuristic algorithms are valuable techniques to extract hidden patterns useful for detecting gender of a text. In this paper, an artificial neural network (ANN) is employed as a classifier to detect the gender of an email author and the whale optimization algorithm (WOA) is used to find optimal weights and biases for improving the accuracy of the ANN classification. Through this combination of ANN and WOA an accuracy of 98%, precision of 97.16%, and recall of 99.67% were achieved, which indicates the superiority of the proposed method on Bayesian networks, regression, decision tree, support vector machine, and ANN examined. INDEX TERMS Author gender detection, machine learning, artificial neural network, whale optimization algorithm.

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“…Figure 4 displays the humpback whale's bubble-net feeding behavior. The WOA comprises three operational steps of shrinking encircling hunt, exploitation (i.e., the bubble-net attacking), and exploration (i.e., searching for the prey) [25,26]. In this algorithm, since there is no information about the optimal hunting place, the target prey is considered as the most appropriate candidate for the problem solution.…”

Section: Whale Optimization Algorithmmentioning

confidence: 99%

Novel Nature-Inspired Hybrids of Neural Computing for Estimating Soil Shear Strength

et al. 2019

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This paper focuses on the prediction of soil shear strength (SSS), which is one of the most fundamental parameters in geotechnical engineering. Consisting of 12 influential factors, namely depth of sample, percentage of sand, percentage of loam, percentage of clay, percentage of moisture content, wet density, dry density, void ratio, liquid limit, plastic limit, plastic Index, and liquidity index as input variables, as well as the shear strength as the desired output, the dataset is provided through a field survey in Vietnam. Thereafter, as for used intelligent techniques, the main focus of the current study is on evaluating the efficiency of three novel optimization techniques for optimizing an artificial neural network (ANN) in predicting the SSS. To this end, the dragonfly algorithm (DA), whale optimization algorithm (WOA), and invasive weed optimization (IWO) are synthesized with ANN to prevail its computational drawbacks. The complexity of the models is optimized by sensitivity analysis. The results confirmed the effectiveness of all three applied algorithms, as the learning error was reduced by nearly 17%, 27%, and 32%, respectively by functioning the DA, WOA, and IWO. As for the testing phase, the IWO and DA achieved a close prediction accuracy. Overall, due to the superiority of the IWO-ANN ensemble, this model could be a promising alternative to traditional methods of shear strength determination.

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A Cloud-based Conceptual Framework for Multi-Objective Virtual Machine Scheduling using Whale Optimization Algorithm

Cited by 14 publications

References 10 publications

A Secure and Multi-objective Virtual Machine Placement Framework for Cloud Data Centre

A Secure and Multi-objective Virtual Machine Placement Framework for Cloud Data Centre

An Author Gender Detection Method Using Whale Optimization Algorithm and Artificial Neural Network

Novel Nature-Inspired Hybrids of Neural Computing for Estimating Soil Shear Strength

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