Credit Based Scheduling Algorithm in Cloud Computing Environment
Cloud computing in today's world has become synonymous with good service policies. In order to achieve good services from a cloud, the need for a number of resources arose. But cloud providers are limited by the amount of resources they have, and are thus compelled to strive to maximum utilizat...
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| Veröffentlicht in: | Procedia computer science Jg. 46; S. 913 - 920 |
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Elsevier B.V
2015
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| Abstract | Cloud computing in today's world has become synonymous with good service policies. In order to achieve good services from a cloud, the need for a number of resources arose. But cloud providers are limited by the amount of resources they have, and are thus compelled to strive to maximum utilization. Min-Min algorithm is used to reduce the make span of tasks by considering the task length. Keeping this in mind, cloud providers should achieve user satisfaction. Thus research favors scheduling algorithms that consider both user satisfaction and resources availability. In this paper an improved scheduling algorithm is introduced after analyzing the traditional algorithms which are based on user priority and task length. High prioritized tasks are not given any special importance when they arrive. The proposed approach considers all of these factors. The experimental results show a considerable improvement in the utilization of resources. |
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| AbstractList | Cloud computing in today's world has become synonymous with good service policies. In order to achieve good services from a cloud, the need for a number of resources arose. But cloud providers are limited by the amount of resources they have, and are thus compelled to strive to maximum utilization. Min-Min algorithm is used to reduce the make span of tasks by considering the task length. Keeping this in mind, cloud providers should achieve user satisfaction. Thus research favors scheduling algorithms that consider both user satisfaction and resources availability. In this paper an improved scheduling algorithm is introduced after analyzing the traditional algorithms which are based on user priority and task length. High prioritized tasks are not given any special importance when they arrive. The proposed approach considers all of these factors. The experimental results show a considerable improvement in the utilization of resources. |
| Author | Thomas, Antony Krishnalal, G. Jagathy Raj, V.P. |
| Author_xml | – sequence: 1 givenname: Antony surname: Thomas fullname: Thomas, Antony email: antonythomas.info@gmail.com organization: Amal Jyothi College of Engineering,Mahatma Gandhi University, Kottayam, India – sequence: 2 givenname: G. surname: Krishnalal fullname: Krishnalal, G. organization: Amal Jyothi College of Engineering,Mahatma Gandhi University, Kottayam, India – sequence: 3 givenname: V.P. surname: Jagathy Raj fullname: Jagathy Raj, V.P. organization: School of Management Studies,Cochin University of Science And Technology, Cochin, India |
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| Cites_doi | 10.1109/CIT.2010.237 10.5120/2403-3197 10.1109/CCGRID.2005.1558639 10.1109/HPCC.2008.172 10.1109/UCC.2012.33 10.1006/jpdc.2000.1714 10.1109/CSC.2011.6138559 10.1007/s00354-008-0081-5 10.1109/NOMS.2012.6212068 10.1016/j.future.2009.07.003 10.1109/ISSP.2013.6526925 10.1109/PDCAT.2011.1 |
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| References | K. Kambatla, A. Pathak, and H. Pucha. Towards optimizing Hadoop provisioning in the cloud in USENIX Workshop on Hot. Topics in Cloud Computing (HotCloud09); 2009. J. Wang. Soft Real-Time Switched Ethernet: Best-Effort Packet Scheduling Algorithm, Implementation, and Feasibility Analysis. Master's thesis. Virginia Tech; 2002. . Liang Luo, Wenjun Wu,Dichen Di, Fei Zhang, Yizhou Yan, Yaokuan Mao. A Resource Scheduling Algorithm of Cloud. Computing based on Energy Efficient Optimization Methods. Chandrashekhar S. Pawar, Rajnikant B. Wagh. Priority Based Dynamic Resource Allocation in Cloud Computing with Modified Waiting Queue; 2013. International Conference on Intelligent Systems and Signal Processing (ISSP). H. P. Borges, J. N de Souza, B. Schulze and A. R. Mury. Automatic generation of platforms in cloud computing in Proceedings of the IEEE Network Operations and Management Symposium (NOMS 12) 2012.pp. 1311-1318. . J. Dean and S. Ghemawat. MapReduce: Simplified data processing on large clusters, in USENIX Symposium on Operating Systems Design and Implementation, San Francisco: CA; Dec 2004. pp. 137-150. . Rajkumar Buyya, Chee Shin Yeo, Srikumar Venugopal. Market-Oriented Cloud Computing: Vision, Hype, and Reality for. Delivering IT Services as Computing Utilities. GRIDS Lab Technical Report; August 2008. Bo Yang, Xiaofei Xu, Feng Tan, Dong Ho Park. An Utility-Based Job Scheduling Algorithm for Cloud Computing Considering. Reliability Factor; 2011 International Conference on Cloud and Service Computing. Braun T.D, Siegel H.J, Beck N, Boloni L.L, Maheswaran M, Reuther A.I, Robertson J.P.et al. A comparison of eleven static .heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. Journal of Parallel and Distributed. Computing. Vol. 61. No. 6, pp.810-837; 2001. Marcos D. Assuncao, Marco A. S. Netto, Fernando Koch, Silvia Bianchi. Context-aware Job Scheduling for. Cloud Computing Environments. 2012 IEEE/ACM Fifth International Conference on Utility and Cloud Computing. Lizhe Wang, Gregor von Laszewski, Andrew Younge, Xi He, Marcel Kunze, Jie Tao and Cheng Fu. Cloud Computing: a Perspective. Study. New Generation Computing. Volume 28, Number 2; 137-146. Yong Dong .Power Measurements and Analyses of Massive Object Storage System. Computer and Information Technology (CIT); 2010. IEEE 10th International Conference pp. 1317-1322 ; 2010. Qi cao,Zhi-bo Wei,Wen-mao Gong. An Optimized Algorithm for Task Scheduling Based On Activity Based Costing in Cloud Computing. J Blythe, S Jain, E Deelman, Y Gil, K Vahi. Task scheduling strategies for workflow-based applications in grids. Cluster. Computing and the Grid; 2005. Zhi Yang, Changqin Yin, Yan Liu. A Cost-based Resource Scheduling Paradigm in Cloud Computing. 2011-12th International Conference on Parallel and Distributed Computing. Applications and Technologies. T Kokilavani, GA DI. Load Balanced Min-Min Algorithm for Static Meta-Task Scheduling in Grid Computing. International Journal of. Computer Applications. Number 2 - Article 7; 2011. S. K. Garg, R. Buyya, and H. J. Siegel. Time and cost trade off management for scheduling parallel applications on utility grids. Future Generation Computer System. 26(8):1344-1355; 2010. Dantong Yu and Thomas G. Robertazzi . Divisible Load Scheduling for Grid Computing. PDCS; 2003, 15th Int’l. Conf. Parallel and Distributed Computing and Systems. IASTED. pp. 1-9; 2003. M. Armbrust et aI. Above the Clouds: A Berkeley View of Cloud Computing, technical report. Univ. of California, Berkeley; Feb 2009. Hsin-Yu Shih, Jhih-Jia Huang, Jenq-Shiou Leu. Dynamic Slot-based Task Scheduling Based on Node. Workload in a MapReduce Computation Model. 10.1016/j.procs.2015.02.162_bib0090 10.1016/j.procs.2015.02.162_bib0005 10.1016/j.procs.2015.02.162_bib0015 10.1016/j.procs.2015.02.162_bib0045 10.1016/j.procs.2015.02.162_bib0100 10.1016/j.procs.2015.02.162_bib0055 10.1016/j.procs.2015.02.162_bib0025 10.1016/j.procs.2015.02.162_bib0035 10.1016/j.procs.2015.02.162_bib0030 10.1016/j.procs.2015.02.162_bib0085 10.1016/j.procs.2015.02.162_bib0040 10.1016/j.procs.2015.02.162_bib0095 10.1016/j.procs.2015.02.162_bib0010 10.1016/j.procs.2015.02.162_bib0065 10.1016/j.procs.2015.02.162_bib0020 10.1016/j.procs.2015.02.162_bib0075 10.1016/j.procs.2015.02.162_bib0070 10.1016/j.procs.2015.02.162_bib0080 10.1016/j.procs.2015.02.162_bib0050 10.1016/j.procs.2015.02.162_bib0060 |
| References_xml | – reference: Zhi Yang, Changqin Yin, Yan Liu. A Cost-based Resource Scheduling Paradigm in Cloud Computing. 2011-12th International Conference on Parallel and Distributed Computing. Applications and Technologies. – reference: M. Armbrust et aI. Above the Clouds: A Berkeley View of Cloud Computing, technical report. Univ. of California, Berkeley; Feb 2009. – reference: Rajkumar Buyya, Chee Shin Yeo, Srikumar Venugopal. Market-Oriented Cloud Computing: Vision, Hype, and Reality for. Delivering IT Services as Computing Utilities. GRIDS Lab Technical Report; August 2008. – reference: Bo Yang, Xiaofei Xu, Feng Tan, Dong Ho Park. An Utility-Based Job Scheduling Algorithm for Cloud Computing Considering. Reliability Factor; 2011 International Conference on Cloud and Service Computing. – reference: Lizhe Wang, Gregor von Laszewski, Andrew Younge, Xi He, Marcel Kunze, Jie Tao and Cheng Fu. Cloud Computing: a Perspective. Study. New Generation Computing. Volume 28, Number 2; 137-146. – reference: S. K. Garg, R. Buyya, and H. J. Siegel. Time and cost trade off management for scheduling parallel applications on utility grids. Future Generation Computer System. 26(8):1344-1355; 2010. – reference: K. Kambatla, A. Pathak, and H. Pucha. Towards optimizing Hadoop provisioning in the cloud in USENIX Workshop on Hot. Topics in Cloud Computing (HotCloud09); 2009. – reference: Braun T.D, Siegel H.J, Beck N, Boloni L.L, Maheswaran M, Reuther A.I, Robertson J.P.et al. A comparison of eleven static .heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. Journal of Parallel and Distributed. Computing. Vol. 61. No. 6, pp.810-837; 2001. – reference: J. Wang. Soft Real-Time Switched Ethernet: Best-Effort Packet Scheduling Algorithm, Implementation, and Feasibility Analysis. Master's thesis. Virginia Tech; 2002. . – reference: H. P. Borges, J. N de Souza, B. Schulze and A. R. Mury. Automatic generation of platforms in cloud computing in Proceedings of the IEEE Network Operations and Management Symposium (NOMS 12) 2012.pp. 1311-1318. . – reference: Marcos D. Assuncao, Marco A. S. Netto, Fernando Koch, Silvia Bianchi. Context-aware Job Scheduling for. Cloud Computing Environments. 2012 IEEE/ACM Fifth International Conference on Utility and Cloud Computing. – reference: Yong Dong .Power Measurements and Analyses of Massive Object Storage System. Computer and Information Technology (CIT); 2010. IEEE 10th International Conference pp. 1317-1322 ; 2010. – reference: Liang Luo, Wenjun Wu,Dichen Di, Fei Zhang, Yizhou Yan, Yaokuan Mao. A Resource Scheduling Algorithm of Cloud. Computing based on Energy Efficient Optimization Methods. – reference: Dantong Yu and Thomas G. Robertazzi . Divisible Load Scheduling for Grid Computing. PDCS; 2003, 15th Int’l. Conf. Parallel and Distributed Computing and Systems. IASTED. pp. 1-9; 2003. – reference: Chandrashekhar S. Pawar, Rajnikant B. Wagh. Priority Based Dynamic Resource Allocation in Cloud Computing with Modified Waiting Queue; 2013. International Conference on Intelligent Systems and Signal Processing (ISSP). – reference: Hsin-Yu Shih, Jhih-Jia Huang, Jenq-Shiou Leu. Dynamic Slot-based Task Scheduling Based on Node. Workload in a MapReduce Computation Model. – reference: Qi cao,Zhi-bo Wei,Wen-mao Gong. An Optimized Algorithm for Task Scheduling Based On Activity Based Costing in Cloud Computing. – reference: J. Dean and S. Ghemawat. MapReduce: Simplified data processing on large clusters, in USENIX Symposium on Operating Systems Design and Implementation, San Francisco: CA; Dec 2004. pp. 137-150. . – reference: J Blythe, S Jain, E Deelman, Y Gil, K Vahi. Task scheduling strategies for workflow-based applications in grids. Cluster. Computing and the Grid; 2005. – reference: T Kokilavani, GA DI. Load Balanced Min-Min Algorithm for Static Meta-Task Scheduling in Grid Computing. International Journal of. Computer Applications. Number 2 - Article 7; 2011. – ident: 10.1016/j.procs.2015.02.162_bib0065 doi: 10.1109/CIT.2010.237 – ident: 10.1016/j.procs.2015.02.162_bib0015 doi: 10.5120/2403-3197 – ident: 10.1016/j.procs.2015.02.162_bib0045 – ident: 10.1016/j.procs.2015.02.162_bib0100 doi: 10.1109/CCGRID.2005.1558639 – ident: 10.1016/j.procs.2015.02.162_bib0025 – ident: 10.1016/j.procs.2015.02.162_bib0030 – ident: 10.1016/j.procs.2015.02.162_bib0070 doi: 10.1109/HPCC.2008.172 – ident: 10.1016/j.procs.2015.02.162_bib0085 – ident: 10.1016/j.procs.2015.02.162_bib0040 doi: 10.1109/UCC.2012.33 – ident: 10.1016/j.procs.2015.02.162_bib0080 doi: 10.1006/jpdc.2000.1714 – ident: 10.1016/j.procs.2015.02.162_bib0035 doi: 10.1109/CSC.2011.6138559 – ident: 10.1016/j.procs.2015.02.162_bib0060 doi: 10.1007/s00354-008-0081-5 – ident: 10.1016/j.procs.2015.02.162_bib0005 – ident: 10.1016/j.procs.2015.02.162_bib0055 – ident: 10.1016/j.procs.2015.02.162_bib0050 doi: 10.1109/NOMS.2012.6212068 – ident: 10.1016/j.procs.2015.02.162_bib0075 – ident: 10.1016/j.procs.2015.02.162_bib0090 doi: 10.1016/j.future.2009.07.003 – ident: 10.1016/j.procs.2015.02.162_bib0095 – ident: 10.1016/j.procs.2015.02.162_bib0010 doi: 10.1109/ISSP.2013.6526925 – ident: 10.1016/j.procs.2015.02.162_bib0020 doi: 10.1109/PDCAT.2011.1 |
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| Title | Credit Based Scheduling Algorithm in Cloud Computing Environment |
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