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1860797468122284032
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INTELEK Repository
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Online Access
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https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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2016-09-14 15:17:51
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Restricted Document
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12834
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UniSZA
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| internalnotes |
1. Available from: www.google.com/trends [cited 2014 May 30]. 2. Erkoc MF, Kert SB. Cloud Computing for distributed university campus: A prototype suggestion. International Conference the Future of Education. 3. Kalpeyeva ZB, Mustafina AK. IT-infrastructure of university based on cloud computing. International Journal of Computer Science Issues. 2013 Sept; 10(5):176–9. 4. Vouk MA. Cloud computing issues, research and implementations. Journal of Computing and IT. 2008; 4:235–6. 5. Kart R, Goldstein P, Yanosky R. Cloud computing in higher education. 6. Moothoor J, Bhatt V. A cloud computing solution for universities: Virtual computing lab. IBM Corporation; 2009. 7. White Paper. Introduction to Cloud Computing. Montreal, Quebec, Canada: Dialogic Corporation; 2010. 8. Huth A, Cebula J. The basics of cloud computing. Carnie Mellon University produced for US-CERT, a Government Organization; 2011. 9. Bento A, Bento R. Cloud computing: A new phase in information technology management. Journal of Information Technology Management. 2011; 22(1):39–46. 10. Goyal T, Agrawal A. Host scheduling algorithm using genetic algorithm in cloud environment. International Journal of Research in Engineering and Technology. 2013 June; 1(1): 7–12. 11. Gu J, Hu J, Zhao T, Sun G. A new resource scheduling strategy based on genetic algorithm cloud computing environment. Journal of Computers. 2012 Jan; 7(1): 42–52. 12. Katyal M, Mishra A. Application of selective algorithm for effective resource provisioning in cloud computing environment. International Journal on Cloud Computing: Services and Architecture (IJCCSA). 2014 Feb; 4(1). 13. Agarwal A, Jain S. Efficient optimal algorithm of task scheduling in cloud computing environment. International Journal of Computer Trends and Technology (IJCTT). 2014 Mar; 9(7):344–9. 14. Sun H, Chen S, Jin C, Guo K. Research and simulation of task scheduling algorithm in cloud computing. TELKOMINA. 2013 Nov; 11(11):6664–72.
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norman
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oai_dc
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https://intelek.unisza.edu.my/intelek/pages/view.php?ref=12834
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12834 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=12834 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal image/jpeg inches 96 96 norman 1420 55 55 769 1420x769 2016-09-14 15:17:51 7141-01-FH02-FIK-16-06527.jpg UniSZA Private Access Multi-criteria strategy for job scheduling and resource load balancing in cloud computing environment (Article) Indian Journal of Science and Technology Cloud computing is growing rapidly over the years and it faces challenges especially in resource management. Resource management in cloud computing is necessary due to its distributed nature with different user demands. Quality of Service (QoS), load balancing and throughput are identified as some of the benefits of proper resource management. This research focuses on job scheduling and resource load balancing in cloud environment. We proposed an efficient algorithm based on multi-criteria strategy. The algorithm consists of two main phases. In the first phase the shortest job completion time is measured based on the completion time of three techniques i.e. min-min, max-min and suffrage. Meanwhile in the second phase genetic algorithm is implemented for resource load balancing. Cloud Sim simulator is used to measure the performance and efficiency of the proposed algorithm. The proposed algorithm enhances jobs scheduling and resource load balancing by ensuring an efficient utilization of the available resources. 8 30 Indian Society for Education and Environment Indian Society for Education and Environment 1. Available from: www.google.com/trends [cited 2014 May 30]. 2. Erkoc MF, Kert SB. Cloud Computing for distributed university campus: A prototype suggestion. International Conference the Future of Education. 3. Kalpeyeva ZB, Mustafina AK. IT-infrastructure of university based on cloud computing. International Journal of Computer Science Issues. 2013 Sept; 10(5):176–9. 4. Vouk MA. Cloud computing issues, research and implementations. Journal of Computing and IT. 2008; 4:235–6. 5. Kart R, Goldstein P, Yanosky R. Cloud computing in higher education. 6. Moothoor J, Bhatt V. A cloud computing solution for universities: Virtual computing lab. IBM Corporation; 2009. 7. White Paper. Introduction to Cloud Computing. Montreal, Quebec, Canada: Dialogic Corporation; 2010. 8. Huth A, Cebula J. The basics of cloud computing. Carnie Mellon University produced for US-CERT, a Government Organization; 2011. 9. Bento A, Bento R. Cloud computing: A new phase in information technology management. Journal of Information Technology Management. 2011; 22(1):39–46. 10. Goyal T, Agrawal A. Host scheduling algorithm using genetic algorithm in cloud environment. International Journal of Research in Engineering and Technology. 2013 June; 1(1): 7–12. 11. Gu J, Hu J, Zhao T, Sun G. A new resource scheduling strategy based on genetic algorithm cloud computing environment. Journal of Computers. 2012 Jan; 7(1): 42–52. 12. Katyal M, Mishra A. Application of selective algorithm for effective resource provisioning in cloud computing environment. International Journal on Cloud Computing: Services and Architecture (IJCCSA). 2014 Feb; 4(1). 13. Agarwal A, Jain S. Efficient optimal algorithm of task scheduling in cloud computing environment. International Journal of Computer Trends and Technology (IJCTT). 2014 Mar; 9(7):344–9. 14. Sun H, Chen S, Jin C, Guo K. Research and simulation of task scheduling algorithm in cloud computing. TELKOMINA. 2013 Nov; 11(11):6664–72.
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| spellingShingle |
Multi-criteria strategy for job scheduling and resource load balancing in cloud computing environment (Article)
|
| summary |
Cloud computing is growing rapidly over the years and it faces challenges especially in resource management. Resource management in cloud computing is necessary due to its distributed nature with different user demands. Quality of Service (QoS), load balancing and throughput are identified as some of the benefits of proper resource management. This research focuses on job scheduling and resource load balancing in cloud environment. We proposed an efficient algorithm based on multi-criteria strategy. The algorithm consists of two main phases. In the first phase the shortest job completion time is measured based on the completion time of three techniques i.e. min-min, max-min and suffrage. Meanwhile in the second phase genetic algorithm is implemented for resource load balancing. Cloud Sim simulator is used to measure the performance and efficiency of the proposed algorithm. The proposed algorithm enhances jobs scheduling and resource load balancing by ensuring an efficient utilization of the available resources.
|
| title |
Multi-criteria strategy for job scheduling and resource load balancing in cloud computing environment (Article)
|
| title_full |
Multi-criteria strategy for job scheduling and resource load balancing in cloud computing environment (Article)
|
| title_fullStr |
Multi-criteria strategy for job scheduling and resource load balancing in cloud computing environment (Article)
|
| title_full_unstemmed |
Multi-criteria strategy for job scheduling and resource load balancing in cloud computing environment (Article)
|
| title_short |
Multi-criteria strategy for job scheduling and resource load balancing in cloud computing environment (Article)
|
| title_sort |
multi-criteria strategy for job scheduling and resource load balancing in cloud computing environment (article)
|