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Computational and Mathematical Methods in Medicine
Volume 2015, Article ID 549026, 8 pages
http://dx.doi.org/10.1155/2015/549026
Research Article

G2LC: Resources Autoscaling for Real Time Bioinformatics Applications in IaaS

1School of Computer, National University of Defense Technology, Changsha 410073, China
2National Supercomputer Center, Tianjin 300457, China

Received 17 May 2015; Revised 22 June 2015; Accepted 23 June 2015

Academic Editor: Tao Huang

Copyright © 2015 Rongdong Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. Z. Zhang, Z. Li, K. Wu et al., “VMThunder: fast provisioning of large-scale virtual machine clusters,” IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 12, pp. 3328–3338, 2014. View at Publisher · View at Google Scholar · View at Scopus
  2. J. A. Stankovic, “Misconceptions about real-time computing: a serious problem for next-generation systems,” Computer, vol. 21, no. 10, pp. 10–19, 1988. View at Publisher · View at Google Scholar · View at Scopus
  3. W. Chen, X. Qiao, J. Wei, and T. Huang, “A two-level virtual machine self-reconfiguration mechanism for the cloud computing platforms,” in Proceedings of the 9th IEEE International Conference on Ubiquitous Intelligence & Computing (UIC '12) & 9th IEEE International Conference on Autonomic & Trusted Computing (ATC '12), pp. 563–570, IEEE, Fukuoka, Japan, September 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. R. Hu, J. Jiang, G. Liu, and L. Wang, “Efficient resources provisioning based on load forecasting in cloud,” The Scientific World Journal, vol. 2014, Article ID 321231, 12 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  5. S. Kundu, R. Rangaswami, A. Gulati, M. Zhao, and K. Dutta, “Modeling virtualized applications using machine learning techniques,” ACM SIGPLAN Notices, vol. 47, no. 7, pp. 3–14, 2012. View at Publisher · View at Google Scholar · View at Scopus
  6. H. Mi, H. Wang, Y. Zhou, M. R.-T. Lyu, and H. Cai, “Toward fine-grained, unsupervised, scalable performance diagnosis for production cloud computing systems,” IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 6, pp. 1245–1255, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. H. Nguyen, Z. Shen, and X. Gu, “Agile: elastic distributed resource scaling for infrastructure-as-a-service,” in Proceedings of the USENIX International Conference on Automated Computing (ICAC '13), San Jose, Calif, USA, June 2013.
  8. S. F. Altschul, W. Gish, W. Miller, E. W. Myers, and D. J. Lipman, “Basic local alignment search tool,” Journal of Molecular Biology, vol. 215, no. 3, pp. 403–410, 1990. View at Publisher · View at Google Scholar · View at Scopus
  9. A. Matsunaga and J. A. B. Fortes, “On the use of machine learning to predict the time and resources consumed by applications,” in Proceedings of the 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, pp. 495–504, IEEE Computer Society, Melbourne, Australia, May 2010. View at Publisher · View at Google Scholar
  10. B. Langmead, M. C. Schatz, J. Lin, M. Pop, and S. L. Salzberg, “Searching for SNPs with cloud computing,” Genome Biology, vol. 10, no. 11, article R134, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. S. Zhao, K. Prenger, L. Smith et al., “Rainbow: a tool for large-scale whole-genome sequencing data analysis using cloud computing,” BMC Genomics, vol. 14, no. 1, article 425, 2013. View at Publisher · View at Google Scholar · View at Scopus
  12. G. Minevich, D. S. Park, D. Blankenberg, R. J. Poole, and O. Hobert, “CloudMap: a cloud-based pipeline for analysis of mutant genome sequences,” Genetics, vol. 192, no. 4, pp. 1249–1269, 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. M. C. Schatz, “CloudBurst: highly sensitive read mapping with MapReduce,” Bioinformatics, vol. 25, no. 11, pp. 1363–1369, 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. D. P. Wall, P. Kudtarkar, V. A. Fusaro, R. Pivovarov, P. Patil, and P. J. Tonellato, “Cloud computing for comparative genomics,” BMC Bioinformatics, vol. 11, no. 1, article 259, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. K. Krampis, T. Booth, B. Chapman et al., “Cloud BioLinux: pre-configured and on-demand bioinformatics computing for the genomics community,” BMC Bioinformatics, vol. 13, no. 1, article 42, 2012. View at Publisher · View at Google Scholar · View at Scopus
  16. X. Li, W. Jiang, Y. Jiang, and Q. Zou, “Hadoop applications in bioinformatics,” in Proceedings of the 7th IEEE Open Cirrus Summit (OCS '12), pp. 48–52, IEEE, Beijing, China, June 2012. View at Publisher · View at Google Scholar
  17. P. Widera and N. Krasnogor, “Protein models comparator: scalable bioinformatics computing on the Google App Engine platform,” http://arxiv.org/abs/1102.4293.
  18. C.-L. Hung and G.-J. Hua, “Local alignment tool based on Hadoop framework and GPU architecture,” BioMed Research International, vol. 2014, Article ID 541490, 7 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  19. S. Liu, G. Quan, and S. Ren, “On-line scheduling of real-time services for cloud computing,” in Proceedings of the 6th World Congress on Services (SERVICES '10), IEEE, Miami, Fla, USA, July 2010.
  20. K. H. Kim, A. Beloglazov, and R. Buyya, “Power-aware provisioning of virtual machines for real-time Cloud services,” Concurrency Computation Practice and Experience, vol. 23, no. 13, pp. 1491–1505, 2011. View at Publisher · View at Google Scholar · View at Scopus