GEMM, a Genetic Engineering-Based Mutual Model for Resource Allocation of Grid Computing
Resource selection, sharing, and aggregation are the key functions of grid computing. However, managing the resources in a grid-based environment is a stimulating task. It is necessary to update the topographical dispersal of the resources possessed by the various organisations with proper load dist...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10318124/ |
| _version_ | 1827700458703028224 |
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| author | Sandeep Kumar Sharma Amit Chaurasia Vijay Shankar Sharma Chiranji Lal Chowdhary Shakila Basheer |
| author_facet | Sandeep Kumar Sharma Amit Chaurasia Vijay Shankar Sharma Chiranji Lal Chowdhary Shakila Basheer |
| author_sort | Sandeep Kumar Sharma |
| collection | DOAJ |
| description | Resource selection, sharing, and aggregation are the key functions of grid computing. However, managing the resources in a grid-based environment is a stimulating task. It is necessary to update the topographical dispersal of the resources possessed by the various organisations with proper load distribution, and availability patterns. Different types of Users and servers have specific objectives and needs that could be achieved using a grid environment. This article suggests a cost-effective efficient framework for resource management in grid computing to look at and address the resource management difficulties. The proposed framework has three main functions, which help in grid construction, load balancing, and resource allocation. A Genetic engineering approach has been implemented to establish a relationship between the resource pool and the jobs of the nodes that improve resource utilization. The proposed methodology also optimizes the overall cost by minimizing turnaround time. The results of the proposed research are compared with commonly used algorithms and claim 1.5 to 10% better results. |
| first_indexed | 2024-03-10T14:12:26Z |
| format | Article |
| id | doaj.art-31026abb394a45a482a37d31ca863053 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-10T14:12:26Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-31026abb394a45a482a37d31ca8630532023-11-21T00:01:25ZengIEEEIEEE Access2169-35362023-01-011112853712854810.1109/ACCESS.2023.333327810318124GEMM, a Genetic Engineering-Based Mutual Model for Resource Allocation of Grid ComputingSandeep Kumar Sharma0https://orcid.org/0009-0003-7093-5397Amit Chaurasia1https://orcid.org/0000-0003-2634-5544Vijay Shankar Sharma2https://orcid.org/0000-0002-3493-6574Chiranji Lal Chowdhary3https://orcid.org/0000-0002-5476-1468Shakila Basheer4https://orcid.org/0000-0001-9032-9560Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, Rajasthan, IndiaDepartment of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, Rajasthan, IndiaDepartment of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, Rajasthan, IndiaSchool of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaDepartment of Information Systems, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi ArabiaResource selection, sharing, and aggregation are the key functions of grid computing. However, managing the resources in a grid-based environment is a stimulating task. It is necessary to update the topographical dispersal of the resources possessed by the various organisations with proper load distribution, and availability patterns. Different types of Users and servers have specific objectives and needs that could be achieved using a grid environment. This article suggests a cost-effective efficient framework for resource management in grid computing to look at and address the resource management difficulties. The proposed framework has three main functions, which help in grid construction, load balancing, and resource allocation. A Genetic engineering approach has been implemented to establish a relationship between the resource pool and the jobs of the nodes that improve resource utilization. The proposed methodology also optimizes the overall cost by minimizing turnaround time. The results of the proposed research are compared with commonly used algorithms and claim 1.5 to 10% better results.https://ieeexplore.ieee.org/document/10318124/Crossover operatorgenetic algorithmgrid computingmutation operatorpopulation |
| spellingShingle | Sandeep Kumar Sharma Amit Chaurasia Vijay Shankar Sharma Chiranji Lal Chowdhary Shakila Basheer GEMM, a Genetic Engineering-Based Mutual Model for Resource Allocation of Grid Computing IEEE Access Crossover operator genetic algorithm grid computing mutation operator population |
| title | GEMM, a Genetic Engineering-Based Mutual Model for Resource Allocation of Grid Computing |
| title_full | GEMM, a Genetic Engineering-Based Mutual Model for Resource Allocation of Grid Computing |
| title_fullStr | GEMM, a Genetic Engineering-Based Mutual Model for Resource Allocation of Grid Computing |
| title_full_unstemmed | GEMM, a Genetic Engineering-Based Mutual Model for Resource Allocation of Grid Computing |
| title_short | GEMM, a Genetic Engineering-Based Mutual Model for Resource Allocation of Grid Computing |
| title_sort | gemm a genetic engineering based mutual model for resource allocation of grid computing |
| topic | Crossover operator genetic algorithm grid computing mutation operator population |
| url | https://ieeexplore.ieee.org/document/10318124/ |
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