A variant of teaching‐learning‐based optimization and its application for minimizing the cost of Workflow Execution in the Cloud Computing

Ram, Satya Deo Kumar; Srivastava, Shashank; Mishra, Krishn Kumar

doi:10.1002/cpe.6425

Cited by 11 publications

(12 citation statements)

References 80 publications

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“…as compared to other meta-heuristic algorithms. I-TLBO is compared with the latest variants of TLBO, such as GTLBO, 4 MTLBONew, 5 ATLBO, 6 MTLBO, 7 and other well-known algorithms as SaDE, 8 SSA, 9 SSD, 10 and BOA. 11 Our paper makes the following contributions.…”

Section: Studentmentioning

confidence: 99%

Redefining the learning mechanism in teaching‐learning‐based optimization and its applications for flowtime‐aware‐cost minimizing of the workflow in cloud

Ram

Srivastava

Mishra

2023

Concurrency and Computation

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SummaryTeaching‐learning‐based optimization (TLBO) algorithm is a population‐based meta‐heuristic algorithm that was created to solve single‐objective optimization problems. The teaching‐learning mechanism of a classroom inspires it. TLBO suffers from weak exploration. As a result, its performance is not good for solving multimodal problems. To turn TLBO into a tool for solving multimodal problems and maintaining good diversity, we made significant modifications into the learning process of the fundamental TLBO. The proposed algorithm produces more diverse solutions and works better for solving multimodal problems. This newly created variant of TLBO is called “Intelligent‐Teaching‐Learning‐Based Optimization (I‐TLBO) algorithm.” I‐TLBO's performance is evaluated against the most recent standard benchmark function, CEC‐06, 2019, and it is discovered that I‐TLBO outperforms the other algorithms. After that, I‐TLBO was applied for flowtime‐aware‐cost minimization of the workflow executions in cloud datacenter. To solve these scheduling problems, I‐TLBO and other metaheuristic algorithms are simulated in CloudSim and tested over scientific workflows such as Inspiral, Montage, SIPHT, sample, Cybershake, and Epigenomics workflows. Finally, it is found that I‐TLBO reduces flowtime and cost both by 28.48%, 11.30%, 17.64%, 13.22%, 11.45%, and 14.71% in comparison to the second best performing algorithm while executing the standard workflow in cloud.

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Section: Studentmentioning

confidence: 99%

Redefining the learning mechanism in teaching‐learning‐based optimization and its applications for flowtime‐aware‐cost minimizing of the workflow in cloud

Ram

Srivastava

Mishra

2023

Concurrency and Computation

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“…The TLBO algorithm is inspired by the teaching-learning process in the classroom and has a wide range of applications in solving real-world problems. [30][31][32] Some studies have used TLBO to solve the VMP problem, which reports promising results. Ram et al 30 used TLBO to minimize cost of workflow execution in CDCs.…”

Section: Literature Reviewmentioning

confidence: 99%

“…[30][31][32] Some studies have used TLBO to solve the VMP problem, which reports promising results. Ram et al 30 used TLBO to minimize cost of workflow execution in CDCs. To improve the performance of TLBO in multimodal problems, the authors made major changes to the theory and operators of this algorithm and called it generalized TLBO (GTLBO).…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic placement of virtual machines using an improved multi‐objective teaching‐learning based optimization algorithm in cloud

Wang¹,

Osmani

Mirzaei

2022

Trans Emerging Tel Tech

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Recently, cloud computing is growing rapidly and contributing to the realization of other technologies. Cloud data centers use a lot of energy to host applications, which leads to high operating costs and increased carbon dioxide emissions into the environment. Dynamic consolidation of virtual machines (VMs) into the minimum number of physical servers is an efficient approach to managing energy consumption in cloud. Virtual machines placement (VMP) is an important problem in the proper consolidation of VMs and is the most common way to improve resource utilization. In VMP problem, VMs are consolidated to minimize the number of active physical servers. This consolidation is done using migration of VMs. After placement, the VMs execute on selected physical servers and the underloaded physical servers are off to optimize energy consumption. Basically, the purpose of VMP is to allocate a set of VMs to a subset of physical servers, taking into account some objectives such as reducing energy consumption and violating the service level agreement (SLA). In this article, an improved teaching-learning based optimization (TLBO) algorithm is formulated to solve VMP as a multi-objective problem, which we call VMP-TLBO. VMP-TLBO can perform optimal placement of migrated VMs to physical servers with the purpose of reducing energy consumption and SLA violation. VMP-TLBO is implemented in MATLAB and the experimental results show that proposed algorithm does not violate SLA and compared to the best equivalence algorithm (ie, PAPSO), it improves energy consumption and SLA violation by 1.8% and 5.6%, respectively. In addition, VMP-TLBO consolidates migrated VMs into the minimum number of physical servers to minimize the number of overloaded hosts as much as possible.

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“…Ram et al collected and organized the long-term accumulated online learning data process of a university, summarized the change trend of education and teaching data, predicted the law of change of e-learning data, and compared it with the traditional regression analysis model. us, it reveals the development trajectory of school education and teaching from a deeper level and provides scientific decisions for school development [15]. Chen and Zhao discusses the concept of educational evaluation, evaluation goals, evaluation content, evaluation standards, evaluation methods, and evaluation results under the background of educational big data [16].…”

Section: Related Workmentioning

confidence: 99%

Application of Clustering Algorithm in English Proficiency Evaluation under the Framework of Big Data

Shang

Liang

2022

Mathematical Problems in Engineering

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With the advancement of science and technology as well as the continual improvement of big data analysis technology, the accuracy of traditional data information classification has declined, making it impossible to assess English ability effectively. A competency evaluation model for college English teaching vacancies is built using this information and the big data architecture. The ability of the big data information model is evaluated and feature information of ability constraints is extracted using the predefined constraint parameter index analysis model. Simultaneously, the K-means clustering algorithm is used to cluster and integrate a series of index parameters of English ability using big data, and the English teaching resource allocation plan is completed in accordance with this, allowing for the scientific evaluation of English teaching ability. The results of the studies show that the clustering method utilized in the context of big data can aid in the evaluation of English competence. In the experiment, four test cycles of English teaching skills were set up, and the effectiveness of the English evaluation techniques described in this paper and two classical cluster evaluation methods were compared and tested. The research shows that using the method described in this paper to evaluate English teaching skills can significantly improve the full utilization of data.

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A variant of teaching‐learning‐based optimization and its application for minimizing the cost of Workflow Execution in the Cloud Computing

Cited by 11 publications

References 80 publications

Redefining the learning mechanism in teaching‐learning‐based optimization and its applications for flowtime‐aware‐cost minimizing of the workflow in cloud

Redefining the learning mechanism in teaching‐learning‐based optimization and its applications for flowtime‐aware‐cost minimizing of the workflow in cloud

Dynamic placement of virtual machines using an improved multi‐objective teaching‐learning based optimization algorithm in cloud

Application of Clustering Algorithm in English Proficiency Evaluation under the Framework of Big Data

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