Classification and Merging Techniques to Reduce Brokerage Using Multi-Objective Optimization
Cloud computing is concerned with effective resource utilization and cost optimization. In the existing system, the cost of resources is much higher. To overcome this problem, a new model called Classification and Merging Techniques for Reducing Brokerage Cost (CMRBC) is designed for effective resou...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/15/2/70 |
| _version_ | 1797483547093630976 |
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| author | Dhanalakshmi Bettahalli Kengegowda Srikantaiah Kamidoddi Chowdaiah Gururaj Harinahalli Lokesh Francesco Flammini |
| author_facet | Dhanalakshmi Bettahalli Kengegowda Srikantaiah Kamidoddi Chowdaiah Gururaj Harinahalli Lokesh Francesco Flammini |
| author_sort | Dhanalakshmi Bettahalli Kengegowda |
| collection | DOAJ |
| description | Cloud computing is concerned with effective resource utilization and cost optimization. In the existing system, the cost of resources is much higher. To overcome this problem, a new model called Classification and Merging Techniques for Reducing Brokerage Cost (CMRBC) is designed for effective resource utilization and cost optimization in the cloud. CMRBC has two benefits. Firstly, this is a cost-effective solution to service providers and customers. Secondly, for every job, virtual machine (VM) creations are avoided to reduce brokerage. The allocation, creation or selection of resources of VM is carried out by broker. The main objective is to maximize the resource utilization and minimize brokerage in cloud computing by using Multi-Objective Optimization (MOO). It considered a multi-attribute approach as it has more than two objectives. Likewise, CMRBC implements efficient resource allocation to reduce the usage cost of resources. The outcome of the experiment shows that CMRBC outperforms 60 percent of reduction in brokerage and 10 percent in response time. |
| first_indexed | 2024-03-09T22:48:36Z |
| format | Article |
| id | doaj.art-99de258acfe444bdaaf61ba3ae2b6a34 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-09T22:48:36Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-99de258acfe444bdaaf61ba3ae2b6a342023-11-23T18:24:33ZengMDPI AGAlgorithms1999-48932022-02-011527010.3390/a15020070Classification and Merging Techniques to Reduce Brokerage Using Multi-Objective OptimizationDhanalakshmi Bettahalli Kengegowda0Srikantaiah Kamidoddi Chowdaiah1Gururaj Harinahalli Lokesh2Francesco Flammini3Department of CSE, BMS Institute of Technology & Management, Bangalore 560064, IndiaDepartment of CSE, SJB Institute of Technology, Bangalore 560060, IndiaDepartment of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru 570002, IndiaIDSIA USI-SUPSI, Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, 6962 Lugano, SwitzerlandCloud computing is concerned with effective resource utilization and cost optimization. In the existing system, the cost of resources is much higher. To overcome this problem, a new model called Classification and Merging Techniques for Reducing Brokerage Cost (CMRBC) is designed for effective resource utilization and cost optimization in the cloud. CMRBC has two benefits. Firstly, this is a cost-effective solution to service providers and customers. Secondly, for every job, virtual machine (VM) creations are avoided to reduce brokerage. The allocation, creation or selection of resources of VM is carried out by broker. The main objective is to maximize the resource utilization and minimize brokerage in cloud computing by using Multi-Objective Optimization (MOO). It considered a multi-attribute approach as it has more than two objectives. Likewise, CMRBC implements efficient resource allocation to reduce the usage cost of resources. The outcome of the experiment shows that CMRBC outperforms 60 percent of reduction in brokerage and 10 percent in response time.https://www.mdpi.com/1999-4893/15/2/70classificationcloud computingmergingvirtual machineoptimization |
| spellingShingle | Dhanalakshmi Bettahalli Kengegowda Srikantaiah Kamidoddi Chowdaiah Gururaj Harinahalli Lokesh Francesco Flammini Classification and Merging Techniques to Reduce Brokerage Using Multi-Objective Optimization Algorithms classification cloud computing merging virtual machine optimization |
| title | Classification and Merging Techniques to Reduce Brokerage Using Multi-Objective Optimization |
| title_full | Classification and Merging Techniques to Reduce Brokerage Using Multi-Objective Optimization |
| title_fullStr | Classification and Merging Techniques to Reduce Brokerage Using Multi-Objective Optimization |
| title_full_unstemmed | Classification and Merging Techniques to Reduce Brokerage Using Multi-Objective Optimization |
| title_short | Classification and Merging Techniques to Reduce Brokerage Using Multi-Objective Optimization |
| title_sort | classification and merging techniques to reduce brokerage using multi objective optimization |
| topic | classification cloud computing merging virtual machine optimization |
| url | https://www.mdpi.com/1999-4893/15/2/70 |
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