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Payload fragmentation framework for high-performance computing in cloud environment

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Abstract

Cloud computing environment delivers resources such as CPU cycles, storage, network service and memory through a web-based model. Users run their applications on remote infrastructure and get benefited more at a lesser cost. Compiling large-scale applications such as compiling GCC and compiling Clang in C and C++ environments where applications involve encryption breaking tools require extensive analysis. Lack of CPU cycles in consumer-grade computing will be detrimental to such applications. Fragmenting and distributing payloads across multiple clusters for such applications in a cloud-like environment is a challenging task. We have proposed a novel approach to perform payload distribution for users who want to run their computationally expensive tasks efficiently. The proposed framework has performed well compared to the ‘traditional’ payload execution policy.

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Abbreviations

CPU:

Central processing unit

GCC:

GNU compiler collection

References

  1. Mell P, Grance T (2011) The NIST definition of cloud computing (Draft)recommendations of the National Institute of Standards and Technology. NIST Special, 145(6),7. National Institute of Standards and Technology, Information Technology Laboratory. http://csrc.nist.gov/publications/drafts/800145/Draft-SP00145-clouddefinition.pdf

  2. Garg SK, Vecchiola C, Buyya R (2013) Mandi: a market exchange for trading utility and cloud computing services. J Supercomput 64(3):1153–1174

    Article  Google Scholar 

  3. Sadiku MNO, Musa SM, Momoh OD (2014) Cloud computing: opportunities and challenges. IEEE Potentials 33(1):34–36. https://doi.org/10.1109/mpot.2013.2279684

    Article  Google Scholar 

  4. Sunilkumar SM, Gopal KS (2014) Resource management for Infrastructure as a Service (IaaS) in cloud computing: a survey review article. J Netw Comput Appl 41:424–440

    Article  Google Scholar 

  5. Di Modica G, Tomarchio O (2016) Matchmaking semantic security policies in heterogeneous clouds. Future Gener Comput Syst 55:176–185

    Article  Google Scholar 

  6. Magoules F, Pan J, Teng F (2013) Cloud computing: data-intensive computing and scheduling. Chapman and Hall/CRC Numerical Analysis and Scientific Computing, London

    Google Scholar 

  7. Chaisiri S, Lee B-S, Niyato D (2012) Optimization of resource provisioning cost in cloud computing. IEEE Trans Serv Comput 5(2):164–177. https://doi.org/10.1109/tsc.2011.7

    Article  Google Scholar 

  8. Amazon EC2 Reserved Instances. http://www.forbes.com/sites/joemckendrick/2016/11/13/with-internet-of-things-and-big-data-92-of-everything-we-do-will-be-in-the-cloud/hash639f6196593f

  9. Chlipala A (2015) An optimizing compiler for a purely functional web-application language. In: Proceedings of the 20th ACM SIGPLAN International Conference On Functional Programming (ICFP 2015). ACM, New York, NY, USA, pp 10–21

  10. Chebolu NABS, Wankar R (2015) A novel scheme for compiler optimization framework. In: 2015 International Conference on Advances in Computing, Communications, and Informatics (ICACCI), Kochi, 2015, pp 2374–2380

  11. Buyya R, Cortes T, Jin H (2001) Single system image. Int J High Perform Comput Appl 15(2):124–135

    Article  Google Scholar 

  12. Hendriks E (2002) BProc: the Beowulf distributed process space. In: ACM Proceedings of ICS, 2002

  13. Wang K, Zhou X, Qiao K, Lang M, McClelland B, Raicu I (2015) Towards scalable distributed workload manager with monitoring-based weakly consistent resource stealing. In: Proceedings of the 24th international symposium on high-performance parallel and distributed computing (HPDC’15). ACM, New York, NY, USA, pp 219–222

  14. Single system image (SSI). http://www.cloudbus.org/papers/SSI-CCWhitePaper.pdf. Accessed 10 June 2014

  15. OpenMosix used to be distributed as a Gentoo Linux kernel choice, but it was removed from Gentoo Linux's Portage tree in February. http://www.openmosix.org/. Accessed 21 June 2014

  16. de Robles MYB, Arnejo ZO, Pabico JP (2015) On web-grid implementation using single system image. Computer science—distributed, parallel, and cluster computing. arXiv:1507.01067

  17. The openMosix Project. http://openmosix.sourceforge.net/. Accessed 28 Sept 2014

  18. Vallee G, Scott SL, Morin C, Berthou JY, Prisker H (2005) SSI-OSCAR: a cluster distribution for high-performance computing using a single system image. In: 19th International symposium on high-performance computing systems and applications (HPCS’05), 2005, pp 319–325

  19. Healy P, Lynn T, Barrett E, Morrison JP (2016) Single system image. J. Parallel Distrib Comput 90(2):35–51. https://doi.org/10.1016/j.jpdc.2016.01.004

    Article  Google Scholar 

  20. Vivek V, Srinivasan R, Rajsingh EB (2013) Resource provisioning methodologies: an approach of producer and consumer favorable in cloud environment. Int J Emerg Technol Adv Eng 3(4):8–13

    Google Scholar 

  21. Li J, Qiu M, Niu J-W, Chen Y, Ming Z (2010) Adaptive resource allocation for preemptable jobs in cloud systems. In: IEEE 2010 10th International Conference on Intelligent Systems Design and Applications (ISDA), Nov 29 2010–Dec 1 2010, pp 31–36

  22. Baker TP (1990) A stack-based resource allocation policy for real-time processes. In: Real-time systems symposium, 1990. IEEE Proceedings, 11th, 5–7 Dec 1990, pp 191–200

  23. Kim T-H, Adeli H, Cho H-S, Gervasi O, Yau SS, Kang B-H, Villalba JG (2011) A dynamic resource allocation model for virtual machine management on cloud. Springer, Berlin

    Google Scholar 

  24. Clark C, Fraser K, Hand S, Hansen JG, Jul E, Limpach C, Pratt I, Warfield A (2005) Live migration of virtual machines. In: Proceedings of the 2nd Conference On Symposium on Networked Systems Design and Implementation (NSDI’05), vol 2. USENIX Association, Berkeley, CA, USA, pp 273–286

  25. Voorsluys W, Broberg J, Venugopal S, Buyya R (2009) Cost of virtual machine live migration in clouds: a performance evaluation. In: Gilje Jaatun M, Zhao G, Rong C (eds) Proceedings of the 1st International Conference on Cloud Computing (CloudCom’09). Springer, Berlin, pp 254–265

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Author notes

  1. V. Vivek

    Present address: CSE, Jain University, Bangalore, Karnataka, 562112, India

Authors and Affiliations

  1. CSE, Karunya Institute of Technology and Science, Coimbatore, Tamilnadu, 641114, India

    V. Vivek, R. Srinivasan & R. Elijah Blessing

  2. Syed Ammal Engineering College, Ramanathapuram, Tamilnadu, 623502, India

    R. Dhanasekaran

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  1. V. Vivek
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  2. R. Srinivasan
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Correspondence to V. Vivek.

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Vivek, V., Srinivasan, R., Elijah Blessing, R. et al. Payload fragmentation framework for high-performance computing in cloud environment. J Supercomput 75, 2789–2804 (2019). https://doi.org/10.1007/s11227-018-2660-7

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