Analysis of Measures to Achieve Resilience during Virtual Machine Interruptions in IaaS Cloud Service

Priya Vedhanayagam, Subha S., Balamurugan Balusamy, P. Vijayakumar, Victor Chang

2017

Abstract

In cloud computing era, the resilience issues faced by cloud computing services may be high. And therefore, the best alternative to reckon with the effects on the Quality-of-Service is to preserve resilience of Cloud computing service. To address this issue, an analytical model is proposed to study queueing system to handle various virtual machine interruptions. The proposed model recommends a secondary virtual machine to redeem the primary virtual machine during a probable halt. The work highlights the innovation employed for analysing the measures to achieve resilience during virtual machine interruptions in IaaS cloud service, the main objective of this research. The model is simulated using SHARPE and the results declare guaranteed performance for the IaaS clients to achieve high availability of service as the response time never deflate during VM interruptions.

References

  1. Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., & Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50-58.
  2. Baba, Y. (1987). On the Mx/G/1 queue with and without vacation time under non-preemptive last-come firstserved discipline. J. of Opns. Res. Society of Japan, 30, 150-159.
  3. Bacigalupo, D. A., Van Hemert, J., Chen, X., Usmani, A., Chester, A. P., He, L., ... & Jarvis, S. A. (2011). Managing dynamic enterprise and urgent workloads on clouds using layered queuing and historical performance models. Simulation Modelling Practice and Theory, 19(6), 1479-1495.
  4. Bruneo, D. (2014). A stochastic model to investigate data center performance and qos in iaas cloud computing systems. IEEE Transactions on Parallel and Distributed Systems, 25(3), 560-569.
  5. Bruneo, D., Distefano, S., Longo, F., & Scarpa, M. (2010, April). Qos assessment of ws-bpel processes through non-markovian stochastic petri nets. In Parallel & Distributed Processing (IPDPS), 2010 IEEE International Symposium on (pp. 1-12). IEEE.
  6. Bruneo, D., Distefano, S., Longo, F., & Scarpa, M. (2013c, October). Stochastic evaluation of QoS in service-based systems. IEEE Transactions on Parallel and Distributed Systems, 24(10), 2090-2099.
  7. Bruneo, D., Distefano, S., Longo, F., Puliafito, A., & Scarpa, M. (2013a, June). Workload-based software rejuvenation in cloud systems. IEEE Transactions on Computers, 62(6), 1072-1085.
  8. Bruneo, D., Lhoas, A., Longo, F., & Puliafito, A. (2013b, September). Analytical evaluation of resource allocation policies in green iaas clouds. InCloud and Green Computing (CGC), 2013 Third International Conference on(pp. 84-91). IEEE.
  9. Bruneo, D., Longo, F., & Puliafito, A. (2011, June). Evaluating energy consumption in a cloud infrastructure. In World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2011 IEEE International Symposium on a(pp. 1-6). IEEE.
  10. Cao, J., Li, K., & Stojmenovic, I. (2014). Optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers. IEEE Transactions on Computers, 63(1), 45-58.
  11. Cheng, C., Li, J., & Wang, Y. (2015). An energy-saving task scheduling strategy based on vacation queuing theory in cloud computing. Tsinghua Science and Technology, 20(1), 28-39.
  12. Ghosh, R., Longo, F., Frattini, F., Russo, S., & Trivedi, K. S. (2014a, March). Scalable analytics for iaas cloud availability. IEEE Transactions on Cloud Computing, 2(1), 57-70.
  13. Ghosh, R., Longo, F., Naik, V. K., & Trivedi, K. S. (2010a, October). Quantifying resiliency of iaas cloud. In Reliable Distributed Systems, 2010 29th IEEE Symposium on (pp. 343-347). IEEE.
  14. Ghosh, R., Longo, F., Naik, V. K., & Trivedi, K. S. (2013). Modeling and performance analysis of large scale iaas clouds. Future Generation Computer Systems, 29(5), 1216-1234.
  15. Ghosh, R., Longo, F., Xia, R., Naik, V. K., & Trivedi, K. S. (2014b, December). Stochastic model driven capacity planning for an infrastructure-as-a-service cloud. IEEE Transactions on Services Computing, 7(4), 667-680.
  16. Ghosh, R., Naik, V. K., & Trivedi, K. S. (2011, June). Power-performance trade-offs in IaaS cloud: A scalable analytic approach. In 2011 IEEE/IFIP 41st International Conference on Dependable Systems and Networks Workshops (DSN-W) (pp. 152-157). IEEE.
  17. Ghosh, R., Trivedi, K. S., Naik, V. K., & Kim, D. S. (2010b, December). End-to-end performability analysis for infrastructure-as-a-service cloud: An interacting stochastic models approach. In Dependable Computing (PRDC), 2010 IEEE 16th Pacific Rim International Symposium on (pp. 125-132). IEEE.
  18. Guo, L., Yan, T., Zhao, S., & Jiang, C. (2014). Dynamic performance optimization for cloud computing using m/m/m queueing system. Journal of Applied Mathematics, 2014.
  19. Javadi, B., Thulasiraman, P., & Buyya, R. (2013). Enhancing performance of failure-prone clusters by adaptive provisioning of cloud resources. The Journal of Supercomputing, 63(2), 467-489.
  20. Khalaf, R. F., & Belgacem, F. B. M. (2014, December). Extraction of the Laplace, Fourier, and Mellin transforms from the Sumudu transform. In AIP Conf. Proc (Vol. 1637, No. 142, pp. 6-1432).
  21. Khazaei, H., Jelena, M., & Vojislav, B. M. (2013d). Performance evaluation of cloud data centers with batch task arrivals. Communication Infrastructures for Cloud Computing, 199-223.
  22. Khazaei, H., Misic, J., & Misic, V. B. (2011a, June). Modelling of cloud computing centers using m/g/m queues. In 2011 31st International Conference on Distributed Computing Systems Workshops (pp. 87- 92). IEEE.
  23. Khazaei, H., Misic, J., & Misic, V. B. (2011b, December). Performance analysis of cloud centers under burst arrivals and total rejection policy. InGlobal Telecommunications Conference (GLOBECOM 2011), 2011 IEEE (pp. 1-6). IEEE.
  24. Khazaei, H., Misic, J., & Misic, V. B. (2012a). Performance analysis of cloud computing centers using m/g/m/m+ r queuing systems. IEEE Transactions on parallel and distributed systems, 23(5), 936-943.
  25. Khazaei, H., Misic, J., & Misic, V. B. (2013b, November). A fine-grained performance model of cloud computing centers. IEEE Transactions on parallel and distributed systems, 24(11), 2138-2147.
  26. Khazaei, H., Misic, J., & Misic, V. B. (2013c, December). Performance of cloud centers with high degree of virtualization under batch task arrivals. IEEE Transactions on Parallel and Distributed Systems, 24(12), 2429-2438.
  27. Khazaei, H., Mišic, J., Mišic, V. B., & Mohammadi, N. B. (2012b, December). Availability analysis of cloud computing centers. In Global Communications Conference (GLOBECOM), 2012 IEEE (pp. 1957- 1962). IEEE.
  28. Khazaei, H., Mišic, J., Mišic, V. B., & Rashwand, S. (2013a, May). Analysis of a pool management scheme for cloud computing centers. IEEE Transactions on parallel and distributed systems, 24(5), 849-861.
  29. Khomonenko, A. D., & Gindin, S. I. (2014, October). Stochastic models for cloud computing performance evaluation. In Proceedings of the 10th Central and Eastern European Software Engineering Conference in Russia (p. 20). ACM.
  30. Little, J. D., & Graves, S. C. (2008). Little's law. In Building intuition (pp. 81-100). Springer US.
  31. Liu, M., Dou, W., Yu, S., & Zhang, Z. (2015). A decentralized cloud firewall framework with resources provisioning cost optimization. IEEE Transactions on Parallel and Distributed Systems, 26(3), 621-631.
  32. Liu, X., Li, S., & Tong, W. (2015). A queuing model considering resources sharing for cloud service performance. The Journal of Supercomputing,71(11), 4042-4055.
  33. Liu, X., Tong, W., Zhi, X., Zhiren, F., & Wenzhao, L. (2014). Performance analysis of cloud computing services considering resources sharing among virtual machines. The Journal of Supercomputing, 69(1), 357- 374.
  34. Liu, X., Zha, Y., Yin, Q., Peng, Y., & Qin, L. (2015). Scheduling parallel jobs with tentative runs and consolidation in the cloud. Journal of Systems and Software, 104, 141-151.
  35. Rimal, B. P., Jukan, A., Katsaros, D., & Goeleven, Y. (2011). Architectural requirements for cloud computing systems: an enterprise cloud approach. Journal of Grid Computing, 9(1), 3-26.
  36. Sousa, E., Lins, F., Tavares, E., Cunha, P., & Maciel, P. (2015). A modeling approach for cloud infrastructure planning considering dependability and cost requirements. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(4), 549-558.
  37. Tian, Y., Lin, C., Chen, Z., Wan, J., & Peng, X. (2013). Performance evaluation and dynamic optimization of speed scaling on web servers in cloud computing. Tsinghua Science and Technology, 18(3), 298-307.
  38. Trivedi, K. S., & Sahner, R. (2009). SHARPE at the age of twenty two. ACM SIGMETRICS Performance Evaluation Review, 36(4), 52-57.
  39. Vilaplana, J., Solsona, F., Teixidó, I., Mateo, J., Abella, F., & Rius, J. (2014). A queuing theory model for cloud computing. The Journal of Supercomputing, 69(1), 492-507.
  40. VMware vLockstep, VMware Inc. vSphere ESX and ESXi Info Center. http://www.vmware. com/products/esxiand-esx/overview.html, July 2017.
  41. Wang, L., Ranjan, R., Chen, J., & Benatallah, B. (Eds.). (2011). Cloud computing: methodology, systems, and applications. CRC Press.
  42. Xia, Y., Zhou, M., Luo, X., Pang, S., & Zhu, Q. (2015a, January). A stochastic approach to analysis of energyaware DVS-enabled cloud datacenters. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(1), 73-83.
  43. Xia, Y., Zhou, M., Luo, X., Pang, S., & Zhu, Q. (2015b, April). Stochastic modeling and performance analysis of migration-enabled and error-prone clouds. IEEE Transactions on Industrial Informatics, 11(2), 495- 504.
  44. Yang, B., Tan, F., & Dai, Y. S. (2013). Performance evaluation of cloud service considering fault recovery. The Journal of Supercomputing, 65(1), 426- 444.
  45. Zhang, S., Qian, Z., Luo, Z., Wu, J., & Lu, S. (2016). Burstiness-Aware Resource Reservation for Server Consolidation in Computing Clouds. IEEE Transactions on Parallel and Distributed Systems, 27(4), 964-977.
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Paper Citation


in Harvard Style

Vedhanayagam P., S. S., Balusamy B., Vijayakumar P. and Chang V. (2017). Analysis of Measures to Achieve Resilience during Virtual Machine Interruptions in IaaS Cloud Service . In Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security - Volume 1: WICSPIT, ISBN 978-989-758-245-5, pages 449-460. DOI: 10.5220/0006419904490460


in Bibtex Style

@conference{wicspit17,
author={Priya Vedhanayagam and Subha S. and Balamurugan Balusamy and P. Vijayakumar and Victor Chang},
title={Analysis of Measures to Achieve Resilience during Virtual Machine Interruptions in IaaS Cloud Service },
booktitle={Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security - Volume 1: WICSPIT,},
year={2017},
pages={449-460},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006419904490460},
isbn={978-989-758-245-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security - Volume 1: WICSPIT,
TI - Analysis of Measures to Achieve Resilience during Virtual Machine Interruptions in IaaS Cloud Service
SN - 978-989-758-245-5
AU - Vedhanayagam P.
AU - S. S.
AU - Balusamy B.
AU - Vijayakumar P.
AU - Chang V.
PY - 2017
SP - 449
EP - 460
DO - 10.5220/0006419904490460