Abstract
To reduce operational costs in computing service, there have been many researches on resource utilization improvement. In cloud environment, virtualization technology, coupled with virtual machine migration, can improve utilization of physical machines by server consolidation. Cloud service providers will consolidate virtual machines in order to reduce the number of physical machines running, therefore reducing their operational cost. Capacity of resources used by virtual machines can be set by users who schedule their tasks, minimizing resource waste by underutilization. However, it is difficult for a user to find the optimal virtual machine with respect to the resource capacity in minimal cost. To solve this problem, cloud service broker is required between users and cloud service providers. Task scheduling in cloud service broker solves finding virtual machine with lowest cost while satisfying SLA. Previous methods using mixed integer programming have showed difficulties in complexity and as system got larger and more complex, they could not solve the problems effectively. In this paper, with preliminary experiment, we propose vector modeling on virtual machine types and tasks can be applied and used in VM management. The allocated computing resources for each task components showed low complexity in operation of VM managements and effectiveness in task consolidation.
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References
Smith, T.F., Waterman, M.S.: Identification of common molecular subsequences. J. Mol. Biol. 147, 195–197 (1981)
May, P., Ehrlich, H.-C., Steinke, T.: ZIB structure prediction pipeline: composing a complex biological workflow through web services. In: Nagel, W.E., Walter, W.V., Lehner, W. (eds.) Euro-Par 2006. LNCS, vol. 4128, pp. 1148–1158. Springer, Heidelberg (2006)
Foster, I., et al.: The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, San Francisco (1999)
Czajkowski, K., et al.: Grid information services for distributed resource sharing. In: 10th IEEE International Symposium on High Performance Distributed Computing, pp. 181–184. IEEE Press, New York (2001)
Foster, I., et al.: The Physiology of the Grid: an Open Grid Services Architecture for Distributed Systems Integration. Technical report, Global Grid Forum (2002)
National Center for Biotechnology Information. http://www.ncbi.nlm.nih.gov
Ren, Y.: A cloud collaboration system with active application control scheme and its experimental performance analysis. In: KAIST (2012)
Kang, D.K., et al.: Enhancing a strategy of virtualized resource assignment in adaptive resource cloud framework. In: Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication. ACM (2013)
Lucas-Simarro, J., et al.: Scheduling strategies for optimal service deployment across multiple clouds. Future Gener. Comput. Syst. 29, 1434–1441 (2012)
Acknowledgement
This work was partly supported by ‘The Cross-Ministry Giga KOREA Project’ grant from the Ministry of Science, ICT and Future Planning, Korea and Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIP) (No. B0101-15-0104, The Development of Supercomputing System for the Genome Analysis)
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© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Joo, Kn., Kim, S., Kang, D., Kim, Y., Jang, H., Youn, CH. (2016). A VM Vector Management Scheme for QoS Constraint Task Scheduling in Cloud Environment. In: Zhang, Y., Peng, L., Youn, CH. (eds) Cloud Computing. CloudComp 2015. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 167. Springer, Cham. https://doi.org/10.1007/978-3-319-38904-2_5
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DOI: https://doi.org/10.1007/978-3-319-38904-2_5
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