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Cost Adaptive VM Management for Scientific Workflow Application in Mobile Cloud

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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.

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References

  1. Srirama SN, Paniagua C, Flores H (2011) Croudstag: social group formation with facial recognition and mobile cloud services. Procedia Comput Sci 5:633–640

    Article  Google Scholar 

  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 198–201

  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 239–242

  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 1037–1040

  5. Chen M (2014) Ndnc-ban: supporting rich media healthcare services via named data networking in cloud-assisted wireless body area networks. Inf Sci 284:142–156

    Article  Google Scholar 

  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 York

  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 161–166

  8. Flores H, Srirama SN (2014) Mobile cloud middleware. J Syst Softw 92:82–94

    Article  Google Scholar 

  9. Montage http://montage.ipac.caltech.edu/

  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 300–319

  11. Yu J, Buyya R (2006) Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms. Sci Program 14(3):217–230

    Google Scholar 

  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):260–274

    Article  Google Scholar 

  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 189–202

  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 111

  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 357–360

  16. Alicherry M, Lakshman TV (2012) Network aware resource allocation in distributed clouds. In: 2012 Proceedings of IEEE, INFOCOM. IEEE, pp 963–971

  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):2219–2235

    Article  Google Scholar 

  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 65

  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 20

  20. Cpu model rank table http://www.cpubenchmark.net/

  21. Openstack foundation http://www.openstack.org/

  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. IEEE

  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 Technology

  24. Burrows-wheeler aligner (bwa) http://bio-bwa.sourceforge.net/

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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.

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Authors and Affiliations

  1. Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea

    Woo-Joong Kim, Dong-Ki Kang, Seong-Hwan Kim & Chan-Hyun Youn

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  1. Woo-Joong Kim
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  2. Dong-Ki Kang
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  3. Seong-Hwan Kim
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  4. Chan-Hyun Youn
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Correspondence to Chan-Hyun Youn.

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Kim, WJ., Kang, DK., Kim, SH. et al. Cost Adaptive VM Management for Scientific Workflow Application in Mobile Cloud. Mobile Netw Appl 20, 328–336 (2015). https://doi.org/10.1007/s11036-015-0593-4

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