Designing a Component-Based Throttled Load Balancing Algorithm for Cloud Data Centers
Cloud services are accessed from different geographical locations where client migration or switching from one server to another based on the loads is a common phenomenon. One of the most critical challenges the cloud data centers face is managing the loads over geographically dispersed data centers...
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| Published in: | Mathematical problems in engineering Vol. 2022; pp. 1 - 12 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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New York
Hindawi
03.10.2022
John Wiley & Sons, Inc |
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| ISSN: | 1024-123X, 1563-5147 |
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| Abstract | Cloud services are accessed from different geographical locations where client migration or switching from one server to another based on the loads is a common phenomenon. One of the most critical challenges the cloud data centers face is managing the loads over geographically dispersed data centers and their virtual machines (VMs). VMs need to be balanced with the varied loads or dynamics of traffic. There are possibilities of the highest loads to be tolerated by the VMs over the cloud servers without crashing. Load balancing issues are managed by load balancing algorithms. Load balancing algorithms have varied issues of efficiency due to certain parameters like the capability of the lowest resource utilization, response time, higher overhead while checking the idle or normal nodes, and many others. Throttled load balancing algorithm manages loads of the virtual machines by dividing the virtual machines into two segments, that is, “available” and “free.” To do this, the throttled algorithm uses a single component to assign the virtual machines and other tasks. The throttled algorithm utilizes only the first VMs available, the next, and so on. These strategic issues most often degrade the performance of the applied load balancing algorithm. Such issues create a curiosity to enhance this algorithm’s performance for efficiently managing the dynamic loads of the cloud VMs. This research paper proposes a component-based throttled load balancing algorithm with VM reader, free VM holder, and free VM manager components. The VM reader component reads all available VMs. The free VM component holds free VMs temporarily until they are moved to the free VM manager component. For the performance test, the cloud analyst simulation tool was used. Based on the comparative analysis with the other five popularly used load balancing algorithms, the component-based algorithm’s performance is significantly enhanced. The proposed algorithm resulted in 325.30-microsecond response time and 27.12-microsecond processing time by the closest data center service broker policy. The newly proposed “component-based throttled load balancing algorithm” is found to be better than the existing throttled algorithm and the other five selected algorithms in terms of response time, processing time, and resource utilization. |
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| AbstractList | Cloud services are accessed from different geographical locations where client migration or switching from one server to another based on the loads is a common phenomenon. One of the most critical challenges the cloud data centers face is managing the loads over geographically dispersed data centers and their virtual machines (VMs). VMs need to be balanced with the varied loads or dynamics of traffic. There are possibilities of the highest loads to be tolerated by the VMs over the cloud servers without crashing. Load balancing issues are managed by load balancing algorithms. Load balancing algorithms have varied issues of efficiency due to certain parameters like the capability of the lowest resource utilization, response time, higher overhead while checking the idle or normal nodes, and many others. Throttled load balancing algorithm manages loads of the virtual machines by dividing the virtual machines into two segments, that is, “available” and “free.” To do this, the throttled algorithm uses a single component to assign the virtual machines and other tasks. The throttled algorithm utilizes only the first VMs available, the next, and so on. These strategic issues most often degrade the performance of the applied load balancing algorithm. Such issues create a curiosity to enhance this algorithm’s performance for efficiently managing the dynamic loads of the cloud VMs. This research paper proposes a component-based throttled load balancing algorithm with VM reader, free VM holder, and free VM manager components. The VM reader component reads all available VMs. The free VM component holds free VMs temporarily until they are moved to the free VM manager component. For the performance test, the cloud analyst simulation tool was used. Based on the comparative analysis with the other five popularly used load balancing algorithms, the component-based algorithm’s performance is significantly enhanced. The proposed algorithm resulted in 325.30-microsecond response time and 27.12-microsecond processing time by the closest data center service broker policy. The newly proposed “component-based throttled load balancing algorithm” is found to be better than the existing throttled algorithm and the other five selected algorithms in terms of response time, processing time, and resource utilization. |
| Author | Mekonnen, Dawit Sharma, Durga Prasad Sharma, Rakesh Kumar Megersa, Alemayehu |
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| Cites_doi | 10.35940/ijrte.b6101.0710221 10.5815/ijitcs.2017.06.07 10.9790/0661-16168287 10.1016/j.jksuci.2018.01.003 10.17485/ijst/2016/v9i20/85866 10.1504/ijac.2017.10010817 10.4236/jcc.2020.87006 10.1109/UEMCON.2017.8249108 |
| ContentType | Journal Article |
| Copyright | Copyright © 2022 Dawit Mekonnen et al. Copyright © 2022 Dawit Mekonnen et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
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| DOI | 10.1155/2022/4640443 |
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| References | 12 13 16 M. T. Student (8) 2015; 6 S. Gupta (14) 2017; 4 Facebook (19) K. E. Narayana (2) 2011; 4 3 S. M. Lanjewar (4) 2014 H. Yeh (18) 2017 P. Mell (1) 2011 6 M. T. Student (9) 2015 7 D. Thazhathethil (11) 2007; 3297 T. Adityasaisrinivas (15) 2019; 6 B. Patel (17) 2015; 1 D. Yadav (5) 2017; 7 10 |
| References_xml | – volume: 6 start-page: 684 year: 2019 ident: 15 article-title: Analysis of load balancing algorithms using cloud analyst publication-title: International Journal of Recent Technology and Engineering – ident: 16 doi: 10.35940/ijrte.b6101.0710221 – volume: 7 start-page: 1 issue: 2 year: 2017 ident: 5 article-title: A study of intranet over cloud publication-title: International Journal of New Innovations in Engineering and Technology – volume: 6 start-page: 102 issue: 5 year: 2015 ident: 8 article-title: Load balancing model for cloud services based on cloud partitioning using RR algorithm pooja publication-title: International Journal of Electronics, Communications and Computer Engineering – ident: 3 doi: 10.5815/ijitcs.2017.06.07 – volume: 4 issue: 10 year: 2017 ident: 14 article-title: A study on various load balancing algorithms for response time reduction in cloud publication-title: International Journal of Current Engineering and Scientific Research (IJCESR) – year: 2015 ident: 9 article-title: Load Balancing Model for Cloud Services Based on Cloud Partitioning Using RR Algorithm Pooja – volume: 3297 year: 2007 ident: 11 article-title: A model for load balancing by partitioning the public cloud publication-title: International Journal of Innovative Research in Computer and Communication Engineering – year: 2014 ident: 4 article-title: Load Balancing in Public Cloud doi: 10.9790/0661-16168287 – ident: 19 article-title: Facebook World Stats and Penetration in the World - Facebook Statistics – ident: 13 doi: 10.1016/j.jksuci.2018.01.003 – year: 2017 ident: 18 article-title: Comparative Analysis of Load Balancing Algorithms in Cloud Computing – ident: 6 doi: 10.17485/ijst/2016/v9i20/85866 – ident: 12 doi: 10.1504/ijac.2017.10010817 – ident: 7 doi: 10.4236/jcc.2020.87006 – ident: 10 doi: 10.1109/UEMCON.2017.8249108 – volume: 4 start-page: 1475 issue: 2 year: 2011 ident: 2 article-title: A review on different types of deployment models in cloud computing publication-title: International Journal of Innovative Research in Computer and Communication Engineering – volume: 1 start-page: 187 issue: 2 year: 2015 ident: 17 article-title: Various load balancing algorithms in cloud computing publication-title: IJARIIE – start-page: 7 year: 2011 ident: 1 article-title: The NIST-national institute of standars and technology- definition of cloud computing publication-title: NIST Special Publication |
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| SubjectTerms | Algorithms Cloud computing Computer centers Data centers Dynamic loads Fault tolerance Geographical locations Load Load balancing Mathematical problems Performance degradation Performance tests Random access memory Resource utilization Response time Response time (computers) Software Virtual environments |
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| Title | Designing a Component-Based Throttled Load Balancing Algorithm for Cloud Data Centers |
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