Efficient virtual machine placement in cloud computing environment using BSO-ANN based hybrid technique
Cloud computing has revolutionized the way businesses and individuals access and utilize computing resources. Efficient virtual machine placement is a critical aspect of optimizing resource utilization, reducing operational costs, energy consumption, service level agreement and minimum virtual machi...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S111001682401127X |
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| author | Pradeep Singh Rawat Sachin Gaur Varun Barthwal Punti Gupta Debjani Ghosh Deepak Gupta Joel JP C. Rodrigues |
| author_facet | Pradeep Singh Rawat Sachin Gaur Varun Barthwal Punti Gupta Debjani Ghosh Deepak Gupta Joel JP C. Rodrigues |
| author_sort | Pradeep Singh Rawat |
| collection | DOAJ |
| description | Cloud computing has revolutionized the way businesses and individuals access and utilize computing resources. Efficient virtual machine placement is a critical aspect of optimizing resource utilization, reducing operational costs, energy consumption, service level agreement and minimum virtual machine migrations, execution time, and ensuring the overall performance of cloud services. This manuscript introduces a novel approach that combines the creative problem-solving capabilities of brainstorming with the computational power of Artificial Neural Networks (ANN) to address the virtual machine placement problem in cloud environments. In this study, we propose a hybrid technique that leverages the collective intelligence of human brainstorming to generate a diverse set of placement strategies. These strategies are then evaluated, refined, and optimized using an ANN model trained on historical cloud resource allocation workload logs. By integrating the human creative process with the data-driven predictive capabilities of ANN, our approach aims to overcome the limitations of traditional virtual machine placement algorithms, which often struggle to adapt to dynamic workloads and changing resource requirements. The manuscript provides a detailed description of the hybrid technique, including the process of brainstorming for generating placement strategies, data collection and preprocessing, ANN model development, and the integration of these components into an efficient placement system. We present experimental results demonstrating the effectiveness of our approach in optimizing resource allocation, improving service performance, and optimizing resource utilization, reducing energy consumption, service level agreement and minimum virtual machine migrations, reducing execution time compared to existing static, and meta-heuristic methods. The proposed Brain Storming with ANN based Hybrid Technique offers a promising solution for enhancing the efficiency of virtual machine placement in cloud computing environments. BSO-ANN outperforms the existing techniques using performance metrics (energy consumption(Kwh), execution time(ms), SLA violations, and number of migrations). It combines human ingenuity with data-driven insights to adapt to the ever-changing dynamics of cloud workloads. This manuscript contributes to the ongoing research in cloud resource management, offering a practical approach for cloud service providers and organizations to better utilize their resources and enhance the overall quality of cloud-based services. © 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Global Science and Technology Forum Pte Ltd. |
| format | Article |
| id | doaj-art-638b320cb2be49e999b2bb96936beb46 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-638b320cb2be49e999b2bb96936beb462025-01-09T06:13:18ZengElsevierAlexandria Engineering Journal1110-01682025-01-01110145152Efficient virtual machine placement in cloud computing environment using BSO-ANN based hybrid techniquePradeep Singh Rawat0Sachin Gaur1Varun Barthwal2Punti Gupta3Debjani Ghosh4Deepak Gupta5Joel JP C. Rodrigues6School of Computing, DIT University, Dehradun, Uttarakhand, 248001, IndiaComputer Science and Engineering Department, Bipin Tripathi Kumaon Institute of Technology, Dwarahat Distt Almora, Uttarakhand, IndiaDepartment of Information Technology, H N B Garhwal University, India.University College Dublin, Dublin, Ireland; Department of Computer Science and Engineering, Pandit Deendayal Energy University, Gujarat, India; Corresponding author at: University College Dublin, Dublin, Ireland.Department of CSE, Bennett University, Greater Noida, IndiaDepartment of Computer Science & Engineering, Maharaja Agrasen Institute of Technology, Delhi, India; Chitkara University, Punjab, IndiaAmazonas State University, Manaus, AM, BrazilCloud computing has revolutionized the way businesses and individuals access and utilize computing resources. Efficient virtual machine placement is a critical aspect of optimizing resource utilization, reducing operational costs, energy consumption, service level agreement and minimum virtual machine migrations, execution time, and ensuring the overall performance of cloud services. This manuscript introduces a novel approach that combines the creative problem-solving capabilities of brainstorming with the computational power of Artificial Neural Networks (ANN) to address the virtual machine placement problem in cloud environments. In this study, we propose a hybrid technique that leverages the collective intelligence of human brainstorming to generate a diverse set of placement strategies. These strategies are then evaluated, refined, and optimized using an ANN model trained on historical cloud resource allocation workload logs. By integrating the human creative process with the data-driven predictive capabilities of ANN, our approach aims to overcome the limitations of traditional virtual machine placement algorithms, which often struggle to adapt to dynamic workloads and changing resource requirements. The manuscript provides a detailed description of the hybrid technique, including the process of brainstorming for generating placement strategies, data collection and preprocessing, ANN model development, and the integration of these components into an efficient placement system. We present experimental results demonstrating the effectiveness of our approach in optimizing resource allocation, improving service performance, and optimizing resource utilization, reducing energy consumption, service level agreement and minimum virtual machine migrations, reducing execution time compared to existing static, and meta-heuristic methods. The proposed Brain Storming with ANN based Hybrid Technique offers a promising solution for enhancing the efficiency of virtual machine placement in cloud computing environments. BSO-ANN outperforms the existing techniques using performance metrics (energy consumption(Kwh), execution time(ms), SLA violations, and number of migrations). It combines human ingenuity with data-driven insights to adapt to the ever-changing dynamics of cloud workloads. This manuscript contributes to the ongoing research in cloud resource management, offering a practical approach for cloud service providers and organizations to better utilize their resources and enhance the overall quality of cloud-based services. © 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Global Science and Technology Forum Pte Ltd.http://www.sciencedirect.com/science/article/pii/S111001682401127XANN(Artificial Neural Network)BSO(Brain Storming Optimizer)Meta-HeuristicOptimizationSLA (Service Level Aggreement)VM (Virtual Machine) |
| spellingShingle | Pradeep Singh Rawat Sachin Gaur Varun Barthwal Punti Gupta Debjani Ghosh Deepak Gupta Joel JP C. Rodrigues Efficient virtual machine placement in cloud computing environment using BSO-ANN based hybrid technique Alexandria Engineering Journal ANN(Artificial Neural Network) BSO(Brain Storming Optimizer) Meta-Heuristic Optimization SLA (Service Level Aggreement) VM (Virtual Machine) |
| title | Efficient virtual machine placement in cloud computing environment using BSO-ANN based hybrid technique |
| title_full | Efficient virtual machine placement in cloud computing environment using BSO-ANN based hybrid technique |
| title_fullStr | Efficient virtual machine placement in cloud computing environment using BSO-ANN based hybrid technique |
| title_full_unstemmed | Efficient virtual machine placement in cloud computing environment using BSO-ANN based hybrid technique |
| title_short | Efficient virtual machine placement in cloud computing environment using BSO-ANN based hybrid technique |
| title_sort | efficient virtual machine placement in cloud computing environment using bso ann based hybrid technique |
| topic | ANN(Artificial Neural Network) BSO(Brain Storming Optimizer) Meta-Heuristic Optimization SLA (Service Level Aggreement) VM (Virtual Machine) |
| url | http://www.sciencedirect.com/science/article/pii/S111001682401127X |
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