A Hybrid Model for Load Balancing in Cloud Using File Type Formatting
Maintaining accuracy in load balancing using metaheuristics is a difficult task even with the help of recent hybrid approaches. In the existing literature, various optimized metaheuristic approaches are being used to achieve their combined benefits for proper load balancing in the cloud. These appro...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9121263/ |
| _version_ | 1828884902152503296 |
|---|---|
| author | Muhammad Junaid Adnan Sohail Adeel Ahmed Abdullah Baz Imran Ali Khan Hosam Alhakami |
| author_facet | Muhammad Junaid Adnan Sohail Adeel Ahmed Abdullah Baz Imran Ali Khan Hosam Alhakami |
| author_sort | Muhammad Junaid |
| collection | DOAJ |
| description | Maintaining accuracy in load balancing using metaheuristics is a difficult task even with the help of recent hybrid approaches. In the existing literature, various optimized metaheuristic approaches are being used to achieve their combined benefits for proper load balancing in the cloud. These approaches often adopt multi-objective QoS metrics, such as reduced SLA violations, reduced makespan, high throughput, low overload, low energy consumption, high optimization, minimum migrations, and higher response time. The cloud applications are generally computation-intensive and can grow exponentially in memory with the increase in size if no proper effective and efficient load balancing technique is adopted resulting in poor quality solutions. To provide a better load balancing solution in cloud computing, with extensive data, a new hybrid model is being proposed that performs classification on the number of files present in the cloud using file type formatting. The classification is performed using Support Vector Machine (SVM) considering various file formats such as audio, video, text maps, and images in the cloud. The resultant data class provides high classification accuracy which is further fed into a metaheuristic algorithm namely Ant Colony Optimization (ACO) using File Type Formatting FTF for better load balancing in the cloud. Frequently used QoS metrics, such as SLA violations, migration time, throughput time, overhead time, and optimization time are evaluated in the cloud environment and comparative analysis is performed with recent metaheuristics, such as Ant Colony Optimization-Particle Swarm Optimization (ACOPS), Chaotic Particle Swarm Optimization (CPSO), Q- learning Modified Particle Swarm Optimization (QMPSO), Cat Swarm Optimization (CSO) and D-ACOELB. The proposed algorithm outperforms them and provides good performance with scalability and robustness. |
| first_indexed | 2024-12-13T11:16:52Z |
| format | Article |
| id | doaj.art-acbe6c5b15c54b3b8182503a9e61df7d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T11:16:52Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-acbe6c5b15c54b3b8182503a9e61df7d2022-12-21T23:48:36ZengIEEEIEEE Access2169-35362020-01-01811813511815510.1109/ACCESS.2020.30038259121263A Hybrid Model for Load Balancing in Cloud Using File Type FormattingMuhammad Junaid0Adnan Sohail1Adeel Ahmed2https://orcid.org/0000-0001-7733-3370Abdullah Baz3https://orcid.org/0000-0002-8669-6883Imran Ali Khan4Hosam Alhakami5https://orcid.org/0000-0002-4908-5573Department of Computing, Iqra University, Islamabad, PakistanDepartment of Computing, Iqra University, Islamabad, PakistanDepartment of Computer Science, Quaid-i-Azam University, Islamabad, PakistanDepartment of Computer Engineering, College of Computer and Information Systems, Umm Al-Qura University, Makkah, Saudi ArabiaDepartment of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, PakistanDepartment of Computer Science, College of Computer and Information Systems, Umm Al-Qura University, Makkah, Saudi ArabiaMaintaining accuracy in load balancing using metaheuristics is a difficult task even with the help of recent hybrid approaches. In the existing literature, various optimized metaheuristic approaches are being used to achieve their combined benefits for proper load balancing in the cloud. These approaches often adopt multi-objective QoS metrics, such as reduced SLA violations, reduced makespan, high throughput, low overload, low energy consumption, high optimization, minimum migrations, and higher response time. The cloud applications are generally computation-intensive and can grow exponentially in memory with the increase in size if no proper effective and efficient load balancing technique is adopted resulting in poor quality solutions. To provide a better load balancing solution in cloud computing, with extensive data, a new hybrid model is being proposed that performs classification on the number of files present in the cloud using file type formatting. The classification is performed using Support Vector Machine (SVM) considering various file formats such as audio, video, text maps, and images in the cloud. The resultant data class provides high classification accuracy which is further fed into a metaheuristic algorithm namely Ant Colony Optimization (ACO) using File Type Formatting FTF for better load balancing in the cloud. Frequently used QoS metrics, such as SLA violations, migration time, throughput time, overhead time, and optimization time are evaluated in the cloud environment and comparative analysis is performed with recent metaheuristics, such as Ant Colony Optimization-Particle Swarm Optimization (ACOPS), Chaotic Particle Swarm Optimization (CPSO), Q- learning Modified Particle Swarm Optimization (QMPSO), Cat Swarm Optimization (CSO) and D-ACOELB. The proposed algorithm outperforms them and provides good performance with scalability and robustness.https://ieeexplore.ieee.org/document/9121263/ACOclassificationhybrid metaheuristicsload balancingmachine learningSVM |
| spellingShingle | Muhammad Junaid Adnan Sohail Adeel Ahmed Abdullah Baz Imran Ali Khan Hosam Alhakami A Hybrid Model for Load Balancing in Cloud Using File Type Formatting IEEE Access ACO classification hybrid metaheuristics load balancing machine learning SVM |
| title | A Hybrid Model for Load Balancing in Cloud Using File Type Formatting |
| title_full | A Hybrid Model for Load Balancing in Cloud Using File Type Formatting |
| title_fullStr | A Hybrid Model for Load Balancing in Cloud Using File Type Formatting |
| title_full_unstemmed | A Hybrid Model for Load Balancing in Cloud Using File Type Formatting |
| title_short | A Hybrid Model for Load Balancing in Cloud Using File Type Formatting |
| title_sort | hybrid model for load balancing in cloud using file type formatting |
| topic | ACO classification hybrid metaheuristics load balancing machine learning SVM |
| url | https://ieeexplore.ieee.org/document/9121263/ |
| work_keys_str_mv | AT muhammadjunaid ahybridmodelforloadbalancingincloudusingfiletypeformatting AT adnansohail ahybridmodelforloadbalancingincloudusingfiletypeformatting AT adeelahmed ahybridmodelforloadbalancingincloudusingfiletypeformatting AT abdullahbaz ahybridmodelforloadbalancingincloudusingfiletypeformatting AT imranalikhan ahybridmodelforloadbalancingincloudusingfiletypeformatting AT hosamalhakami ahybridmodelforloadbalancingincloudusingfiletypeformatting AT muhammadjunaid hybridmodelforloadbalancingincloudusingfiletypeformatting AT adnansohail hybridmodelforloadbalancingincloudusingfiletypeformatting AT adeelahmed hybridmodelforloadbalancingincloudusingfiletypeformatting AT abdullahbaz hybridmodelforloadbalancingincloudusingfiletypeformatting AT imranalikhan hybridmodelforloadbalancingincloudusingfiletypeformatting AT hosamalhakami hybridmodelforloadbalancingincloudusingfiletypeformatting |