Genetic algorithm for quality of service based resource allocation in cloud computing

Devarasetty, Prasad; Reddy, Satyananda

doi:10.1007/s12065-019-00233-6

Cited by 36 publications

(25 citation statements)

References 20 publications

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“…The uncertainty proposed model provides dynamic resource allocation with efficiency and accuracy in cloud infrastructure and deployment. Prasad Devarasetty [8] proposed need-based resource allocation to implement a genetics algorithm technique with a particular defined budget to achieve QoS. The designed techniques deployed on amazon based cloud with cost limitation and efficient resource allocation.…”

Section: Related Workmentioning

confidence: 99%

Hybrid SFLA-UBS Algorithm for Optimal Resource Provisioning with Cost Management in Multi-cloud Computing

Hussain¹,

He²,

Zhu³

et al. 2021

IJACSA

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Multi-cloud is a vendor-based heterogeneous cloud paradigm in recent era of computing with dynamic infrastructural deployment. Multi-cloud provides all the essential and on-demand requirements of a virtual environment from various domains under a single service level agreement (SLA). Consumers from multitier domains can access all the available resources placed in a shared pool on service provider's side, as per their requirement. The shared pool of resources creates complexity in assigning the best and suitable resource to a particular virtual instance under the same services provider end. The complexity of resources in terms of accessibility from the various domains, dynamic allocation, security, and quality of services (QoS) raises concerns in the multi-cloud infrastructure. This complexity raise concern relates to optimal provisioning and cost management. In the proposed work a hybrid technique with a shuffled leapfrog algorithm and ubiquitous binary search (SLFA-UBS) to resolve these issues with optimal provisioning, dynamic allocation and better resource selection. The proposed work will help to create a need-based and demand-based resource pool with the appropriate selection of each resource. The proposed model also supports resource optimization with dynamic provisioning, cost-effective solution to achieve QoS in multi-cloud deployment on service provider end.

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Section: Related Workmentioning

confidence: 99%

Hybrid SFLA-UBS Algorithm for Optimal Resource Provisioning with Cost Management in Multi-cloud Computing

Hussain¹,

He²,

Zhu³

et al. 2021

IJACSA

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“…The solution can be found using genetic algorithms [7]. They have proven themselves to be useful in assessing the problem of multicriteria optimization in the field of software development: evolutionary software development cost estimation [8]; optimization of computing resources utilization [9,10]; generating optimal test data sets [11]; evaluating [12] software reliability; optimization of software partitioning into modules [13]; prioritizing client requirements for software development [14]; software refactoring [15]; in solving problems of project management [16] and human resources allocation [17]; in other tasks, including those related to the development of cloud webservices with QoS-aware resource allocation and dynamic web-service composition [18][19][20]. : ,…”

Section: Related Workmentioning

confidence: 99%

Software Design using Genetic Quality Components Search

Nikulchev¹,

Ilin²,

Gusev³

2019

IJACSA

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The paper presents a software design methodology based on computational experiments for effective selection of software component set. The selection of components is performed with respect to the numerical quality criteria evaluated in the reproducible experiments with various sets of components in the virtual infrastructure simulating the operating conditions of a software system being developed. To reduce the number of experiments with unpromising sets of components the genetic algorithm is applied. For representing the sets of components in the form of natural genotypes, the encoding mapping is introduced, reverse mapping is used to decipher the genotype. In the first step of the technique, the genetic algorithm creates an initial population of random genotypes that are converted into the assessed sets of software components. The paper shows the application of the proposed methodology to find the effective choice of Node.js components. For this purpose, a MATLAB program of genetic search and experimental scenario for a virtual machine running Ubuntu 16.04 LTS operating system were developed. To guarantee the proper reproduction of the experimental conditions, the Vagrant and Ansible configuration tools were used to create the virtual environment of the experiment.

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“…Cloud Computing has the ability to provide different types of services over geographical regions. Resource allocation in cloud is one of the complex procedures due to the phenomenon of searching best possible combinations of tasks and resources [1][2][3][4][5]. Various methods are developed to solve the problem of resource allocation because different types of on-demand services offered by cloud service providers' causes challenges to the customers for selecting the resources [6,[7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…Resource allocation is represented as algorithms which improves the performance of the computing resources by minimizing the cost, energy consumption, execution time and improves the effectiveness [3]. However, allocating more tasks to single resource leads to performance degrades and it takes more time to execute the tasks which leads to user dissatisfaction.…”

Section: Introductionmentioning

confidence: 99%

Research of Task Management and Resource Allocation in Cloud Computing

2019

IJITEE

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This paper presents the task management framework in which tasks are identified, analysed and grouped based on the user QoS requirements. As a next step, cost and time based scheduling algorithm is employed to select the suitable pair of resources and tasks. The performance of the proposed algorithm is tested with execution time and cost allocated to the resources. Two existing algorithms are considered along with proposed algorithm for performance comparison. The results proved the efficiency of the proposed algorithm.

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Genetic algorithm for quality of service based resource allocation in cloud computing

Cited by 36 publications

References 20 publications

Hybrid SFLA-UBS Algorithm for Optimal Resource Provisioning with Cost Management in Multi-cloud Computing

Hybrid SFLA-UBS Algorithm for Optimal Resource Provisioning with Cost Management in Multi-cloud Computing

Software Design using Genetic Quality Components Search

Research of Task Management and Resource Allocation in Cloud Computing

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