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Mobile Networks and Applications

, Volume 20, Issue 3, pp 328–336 | Cite as

Cost Adaptive VM Management for Scientific Workflow Application in Mobile Cloud

  • Woo-Joong Kim
  • Dong-Ki Kang
  • Seong-Hwan Kim
  • Chan-Hyun Youn
Article

Abstract

In this paper, to guarantee Service Level Agreement composed of the deadline and budget given by users for workflow application services in mobile cloud, we propose the two-phases algorithm with a cost adaptive VM management. Firstly, the greedy based workflow co-scheduling phase schedules a workflow by using a resource consolidation in a parallel manner to decrease a cost with the deadline assurance. Secondly, the resource profiling based placement phase locates a VM to a certain physical host in the multi-cloud using the profile on the property of clouds in order to comply with the budget while maximizing the service quality. We implement mobile cloud brokering system with the two-phases algorithm and demonstrate that our proposed system outperforms traditional cloud systems through several experimental results.

Keywords

Mobile cloud computing Mobile cloud brokering system Cost adaptive resource management 

Notes

Acknowledgments

This research was supported by Next-Generation Information Computing Development Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education, Science and Technology (2012-0020522) and the MSIP (Ministry of Science, ICT & Future Planning), Korea in the ICT R &D Program 2014 and the MSIP under the ITRC support program (NIPA-2014(H0301-14-1020)) supervised by the NIPA.

References

  1. 1.
    Srirama SN, Paniagua C, Flores H (2011) Croudstag: social group formation with facial recognition and mobile cloud services. Procedia Comput Sci 5:633–640CrossRefGoogle Scholar
  2. 2.
    Yang X, Pan T, Shen J (2010) On 3g mobile e-commerce platform based on cloud computing. In: 2010 3rd IEEE International conference on ubi-media computing (U-Media). IEEE, pp 198–201Google Scholar
  3. 3.
    Gao H-Q, Zhai Y-J (2010) System design of cloud computing based on mobile learning. In: 2010 3rd International symposium on knowledge acquisition and modeling (KAM). IEEE, pp 239–242Google Scholar
  4. 4.
    Doukas C, Pliakas T, Maglogiannis I (2010) Mobile healthcare information management utilizing cloud computing and android os. In: 2010 annual International conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 1037–1040Google Scholar
  5. 5.
    Chen M (2014) Ndnc-ban: supporting rich media healthcare services via named data networking in cloud-assisted wireless body area networks. Inf Sci 284:142–156CrossRefGoogle Scholar
  6. 6.
    Chen M, Mao S, Zhang Y, Leung VCM (2014) Big data: related technologies, challenges and future prospects. In: Springer briefs series on wireless communications, 1st edn. Springer, New YorkGoogle Scholar
  7. 7.
    Yao J, Zhang J, Chen S, Wang C, Levy D (2011) Facilitating bioinformatic research with mobile cloud. In: The 2nd International conference on cloud computing, GRIDs, and virtualization, Cloud Computing 2011, pp 161–166Google Scholar
  8. 8.
    Flores H, Srirama SN (2014) Mobile cloud middleware. J Syst Softw 92:82–94CrossRefGoogle Scholar
  9. 9.
  10. 10.
    Oinn T, Li P, Kell DB, Goble C, Goderis A, Greenwood M, Hull D, Stevens R, Turi D, Zhao J (2007) Taverna/mygrid: aligning a workflow system with the life sciences community. In: Workflows for e-Science. Springer, Berlin Heidelberg New York, pp 300–319Google Scholar
  11. 11.
    Yu J, Buyya R (2006) Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms. Sci Program 14(3):217–230Google Scholar
  12. 12.
    Topcuoglu H, Hariri S, Wu M-Y (2002) Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans Parallel Distrib Syst 13(3):260–274CrossRefGoogle Scholar
  13. 13.
    Sakellariou R, Zhao H, Tsiakkouri E, Dikaiakos MD (2007) Scheduling workflows with budget constraints. In: Integrated research in GRID computing. Springer, Berlin Heidelberg New York, pp 189–202Google Scholar
  14. 14.
    Sakellariou R, Zhao H (2004) A hybrid heuristic for dag scheduling on heterogeneous systems. In: Proceedings of 18th International parallel and distributed processing symposium, 2004. IEEE, p 111Google Scholar
  15. 15.
    Zamanifar K, Nasri N, Nadimi-Shahraki M (2012) Data-aware virtual machine placement and rate allocation in cloud environment. In: 2012 2nd International conference on advanced computing & communication technologies (ACCT). IEEE, pp 357–360Google Scholar
  16. 16.
    Alicherry M, Lakshman TV (2012) Network aware resource allocation in distributed clouds. In: 2012 Proceedings of IEEE, INFOCOM. IEEE, pp 963–971Google Scholar
  17. 17.
    Singh K, İpek E, McKee SA, de Supinski BR, Schulz M, Caruana R (2007) Predicting parallel application performance via machine learning approaches. Concurrency and Computation: Practice and Experience 19(17):2219–2235CrossRefGoogle Scholar
  18. 18.
    Kang D-K, Kim S-H, Youn C-H, Chen M (2014) Cost adaptive workflow scheduling in cloud computing. In: Proceedings of the 8th International conference on ubiquitous information management and communication. ACM, p 65Google Scholar
  19. 19.
    Farley B, Juels A, Varadarajan V, Ristenpart T, Bowers KD, Swift MM (2012) More for your money: exploiting performance heterogeneity in public clouds. In: Proceedings of the 3rd ACM symposium on cloud computing. ACM, p 20Google Scholar
  20. 20.
    Cpu model rank table http://www.cpubenchmark.net/
  21. 21.
    Openstack foundation http://www.openstack.org/
  22. 22.
    Yu J, Buyya R, Tham CK (2005) Cost-based scheduling of scientific workflow applications on utility grids. In: 2005 1st International conference on e-Science and grid computing, pp 140–147. IEEEGoogle Scholar
  23. 23.
    Ren Y (2012) A cloud collaboration system with active application control scheme and its experimental performance analysis. Master’s thesis, Korea Advanced Institute of Science and TechnologyGoogle Scholar
  24. 24.
    Burrows-wheeler aligner (bwa) http://bio-bwa.sourceforge.net/

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Woo-Joong Kim
    • 1
  • Dong-Ki Kang
    • 1
  • Seong-Hwan Kim
    • 1
  • Chan-Hyun Youn
    • 1
  1. 1.Department of Electrical Engineering, Korea Advanced Institute of Science and TechnologyDaejeonKorea

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