Improvement for tasks allocation system in VM for cloud datacenter using modified bat algorithm

Ullah, Arif; Chakir, Ahmed

doi:10.1007/s11042-022-12904-1

Cited by 11 publications

(8 citation statements)

References 37 publications

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“…The work in [15] focused on the enhancement of the task distribution in virtual machine for CC utilizing load balancing technique. To achieve this objective modification was made in the Bat algorithm fitness value in the load balancer section.…”

Section: Related Workmentioning

confidence: 99%

Hypervolume Sen Task Scheduilng and Multi Objective Deep Auto Encoder based Resource Allocation in Cloud

George

Anoop

2023

IJRITCC

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Cloud Computing (CC) environment has restructured the Information Age by empowering on demand dispensing of resources on a pay-per-use base. Resource Scheduling and allocation is an approach of ascertaining schedule on which tasks should be carried out. Owing to the heterogeneity nature of resources, scheduling of resources in CC environment is considered as an intricate task. Allocating best resource for a cloud request remains a complicated task and the issue of identifying the best resource – task pair according to user requirements is considered as an optimization issue. Therefore the main objective of the Cloud Server remains in scheduling the tasks and allocating the resources in an optimal manner. In this work an optimized task scheduled resource allocation model is designed to effectively address large numbers of task request arriving from cloud users, while maintaining enhanced Quality of Service (QoS). The cloud user task requests are mapped in an optimal manner to cloud resources. The optimization process is carried out using the proposed Multi-objective Auto-encoder Deep Neural Network-based (MA-DNN) method which is a combination of Sen’s Multi-objective functions and Auto-encoder Deep Neural Network model. First tasks scheduling is performed by applying Hypervolume-based Sen’s Multi-objective programming model. With this, multi-objective optimization (i.e., optimization of cost and time during the scheduling of tasks) is performed by means of Hypervolume-based Sen’s Multi-objective programming. Second, Auto-encoder Deep Neural Network-based Resource allocation is performed with the scheduled tasks that in turn allocate the resources by utilizing Jensen–Shannon divergence function. The Jensen–Shannon divergence function has the advantage of minimizing the energy consumption that only with higher divergence results, mapping is performed, therefore improving the energy consumption to a greater extent. Finally, mapping tasks with the corresponding resources using Kronecker Delta function improves the makespan significantly. To show the efficiency of Multi-objective Auto-encoder Deep Neural Network-based (MA-DNN) cloud time scheduling and optimization between tasks and resources in the CC environment, we also perform thorough experiments on the basis of realistic traces derived from Personal Cloud Datasets. The experimental results show that compared with RAA-PI-NSGAII and DRL, MA-DNN not only significantly accelerates the task scheduling efficiency, task scheduling time but also reduces the energy usage and makespan considerably.

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Section: Related Workmentioning

confidence: 99%

Hypervolume Sen Task Scheduilng and Multi Objective Deep Auto Encoder based Resource Allocation in Cloud

George

Anoop

2023

IJRITCC

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“…In their proposed work, the authors applied a dynamic workload, saving energy consumption and resource wastage at the cloud data center. Ullah et al [23] proposed a task distribution approach using a load balancing technique. The author applied a modified BAT algorithm for load balancing in their proposed method.…”

Section: Background and Related Workmentioning

confidence: 99%

A Novel Energy Efficient Multi-Dimensional Virtual Machines Allocation and Migration at the Cloud Data Center

Sharma,

Bojjagani,

Reddy

et al. 2023

IEEE Access

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Due to the rapid utilization of cloud services, the energy consumption of cloud data centres is increasing dramatically. These cloud services are provided by Virtual Machines (VMs) through the cloud data center. Therefore, energy-aware VMs allocation and migration are essential tasks in the cloud environment. This paper proposes a Branch-and-Price based energy-efficient VMs allocation algorithm and a Multi-Dimensional Virtual Machine Migration (MDVMM) algorithm at the cloud data center. The Branch-and-Price based VMs allocation algorithm reduces energy consumption and wastage of resources by selecting the optimal number of energy-efficient PMs at the cloud data center. The proposed MDVMM algorithm saves energy consumption and avoids the Service Level Agreement (SLA) violation by performing an optimal number of VMs migrations. The experimental results demonstrate that our proposed Branch-and-Price based VMs allocation with VMs migration algorithms saves more than 31% energy consumption and improves 21.7% average resource utilization over existing state-of-the-art techniques with a 95% confidence interval. The performance of the proposed approaches outperforms in terms of SLA violation, VMs migration, and Energy SLA Violation (ESV) combined metrics over existing state-of-the-art VMs allocation and migration algorithms.

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“…Where +1 belongs to the positive predicted class, -1 belongs to the negative predicted class, w is the weight vector and M is the margin width. Consequently, the main achievement through linear SVM can be obtained by the formulation according to the formulas along their properties as (5) [59].…”

Section: 𝑊 𝑋 + + 𝑏 = +1mentioning

confidence: 99%

Generalization of linear and non-linear support vector machine in multiple fields: a review

Khan,

Aznaoui

et al. 2023

Comput Sci Inf Technol

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Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. They belong to a family of generalized linear classifiers. In other terms, SVM is a classification and regression prediction tool that uses machine learning theory to maximize predictive accuracy. In this article, the discussion about linear and non-linear SVM classifiers with their functions and parameters is investigated. Due to the equality type of constraints in the formulation, the solution follows from solving a set of linear equations. Besides this, if the under-consideration problem is in the form of a non-linear case, then the problem must convert into linear separable form with the help of kernel trick and solve it according to the methods. Some important algorithms related to sentimental work are also presented in this paper. Generalization of the formulation of linear and non-linear SVMs is also open in this article. In the final section of this paper, the different modified sections of SVM are discussed which are modified by different research for different purposes.

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Improvement for tasks allocation system in VM for cloud datacenter using modified bat algorithm

Cited by 11 publications

References 37 publications

Hypervolume Sen Task Scheduilng and Multi Objective Deep Auto Encoder based Resource Allocation in Cloud

Hypervolume Sen Task Scheduilng and Multi Objective Deep Auto Encoder based Resource Allocation in Cloud

A Novel Energy Efficient Multi-Dimensional Virtual Machines Allocation and Migration at the Cloud Data Center

Generalization of linear and non-linear support vector machine in multiple fields: a review

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