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Modeling and Analysis of Performance and Energy Consumption in Cloud Data Centers

  • Research Article - Computer Engineering and Computer Science
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Abstract

Recently, the deployment of cloud data centers (CDCs) and the adoption of cloud technologies have transformed the way we do computation, storage and networking. Typically in a CDC, virtual machines (VMs) are allocated to physical machines. Estimating correctly the number of needed VMs to meet a given workload and QoS parameters is important for cost and resource efficiency. In this paper, we develop a queuing model to aid in studying and analyzing performance in CDC. We model the CDC platforms with an open queuing system that can be used to estimate the expected quality of service (QoS) parameters such as the throughput, the drop rate, the CPU utilization and the response time. In addition, we present an energy consumption model to study and estimate the energy consumption in the CDC. We give numerical examples to show how the proposed model estimates the number of needed VMs to meet a given level of QoS parameters. The results obtained from our analysis as well as the simulation models show that the proposed model is able to correctly and effectively estimate the number of VM instances required to achieve QoS targets under different workload conditions.

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References

  1. Voorsluys, W.; Broberg, J.; Buyya, R.: Introduction to cloud computing. Cloud computing: principles and paradigms, pp. 1–44 (2011)

    Google Scholar 

  2. Furht, B.: Cloud Computing Fundamentals. Handbook of Cloud Computing, pp. 3–19. Springer, US (2010)

    Chapter  Google Scholar 

  3. El Kafhali, S.; Salah, K.: Performance analysis of multi-core VMs hosting cloud SaaS applications. Comput. Stand. Interfaces 55, 126–135 (2018)

    Article  Google Scholar 

  4. Huang, W.; Ganjali, A.; Kim, B.H.; Oh, S.; Lie, D.: The state of public infrastructure-as-a-service cloud security. ACM Comput. Surv. 47(4), 68 (2015)

    Article  Google Scholar 

  5. Alam, A.F.; Soltanian, A.; Yangui, S.; Salahuddin, M.A.; Glitho, R.; Elbiaze, H.: A cloud platform-as-a-service for multimedia conferencing service provisioning. In: Proceedings of the 21st IEEE Symposium on Computers and Communications, IEEE ISCC’16, Messina, Italy (2016)

  6. Schafer, J.; Lichter, H.: Changes in requirements engineering after migrating to the software as a service model. In: Full-Scale Software Engineering/Current Trends in Release Engineering, pp. 25–30 (2016)

  7. Amazon, E.: Amazon elastic compute cloud. Retrieved Feb, Vol. 10 (2009)

  8. Ghosh, R.; Trivedi, K.S.; Naik, V.K.; Kim, D.S.: End-to-end performability analysis for infrastructure-as-a-service cloud: an interacting stochastic models approach. In: Proceedings of the 16th Pacific Rim International Symposium on Dependable Computing, PRDC’10, Tokyo, Japan, pp. 125–132 (2010)

  9. Jennings, B.; Stadler, R.: Resource management in clouds: survey and research challenges. J. Netw. Syst. Manag. 23(3), 567–619 (2015)

    Article  Google Scholar 

  10. Kim, W.: Cloud computing: today and tomorrow. J. Object Technol. 8(1), 65–72 (2009)

    Article  Google Scholar 

  11. Chen, H.; Yao, D.D.: Fundamentals of Queueing Networks: Performance, Asymptotics, and Optimization, vol. 46. Springer, Berlin (2013)

    MATH  Google Scholar 

  12. Khojasteh, H.; Misic, J.; Misic, V.B.: Characterizing energy consumption of iaas clouds in non-saturated operation. In: Proceedings of the IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), INFOCOM’14, Toronto, Canada, pp. 398–403 (2014)

  13. Masdari, M.; Nabavi, S.S.; Ahmadi, V.: An overview of virtual machine placement schemes in cloud computing. J. Netw. Comput. Appl. 66, 106–127 (2016)

    Article  Google Scholar 

  14. Wen, Y.-F.: Energy-aware dynamical hosts and tasks assignment for cloud computing. J. Syst. Softw. 115, 144–156 (2016)

    Article  Google Scholar 

  15. Piraghaj, S.F.; Dastjerdi, A.V.; Calheiros, R.N.; Buyya, R.: Efficient virtual machine sizing for hosting containers as a service (services 2015). In: Proceedings of the IEEE 11th World Congress on Services, SERVICES’15, New York, pp. 31–38 (2015)

  16. Xiao, Z.; Jiang, J.; Zhu, Y.; Ming, Z.; Zhong, S.; Cai, S.: A solution of dynamic vms placement problem for energy consumption optimization based on evolutionary game theory. J. Syst. Softw. 101, 260–272 (2015)

    Article  Google Scholar 

  17. Li, K.: Power and performance management for parallel computations in clouds and data centers. J. Comput. Syst. Sci. 82(2), 174–190 (2016)

    Article  MathSciNet  Google Scholar 

  18. Khazaei, H.; Misic, J.; Misic, V.B.: Performance of an iaas cloud with live migration of virtual machines. In: Proceedings of the IEEE Global Communications Conference (GLOBECOM), GLOBECOM’13, Aalanta, USA, pp. 2289–2293 (2013)

  19. Xiong, K.; Perros, H.: Service performance and analysis in cloud computing. In: Proceedings of the 1st IEEE Congress on Services, SERVICES’09, Los Angeles, California, USA, pp. 693–700 (2009)

  20. Guo, L.; Yan, T.; Zhao, S.; Jiang, C.: Dynamic performance optimization for cloud computing using m/m/m queueing system. J. Appl. Math. 2014 (2014)

    Google Scholar 

  21. Bai, W.-H.; Xi, J.-Q.; Zhu, J.-X.; Huang, S.-W.: Performance analysis of heterogeneous data centers in cloud computing using a complex queuing model. Math. Probl. Eng. 2015 (2015)

    MathSciNet  MATH  Google Scholar 

  22. El Kafhali, S.; Salah, K.: Stochastic modelling and analysis of cloud computing data center. In: 20th ICIN Conference Innovations in Clouds, Internet and Networks, IEEE, Paris, France, pp. 122–126 (2017)

  23. Ghosh, R.; Longo, F.; Naik, V.K.; Trivedi, K.S.: Modeling and performance analysis of large scale iaas clouds. Future Gener. Comput. Syst. 29(5), 1216–1234 (2013)

    Article  Google Scholar 

  24. Ghosh, R.; Longo, F.; Xia, R.; Naik, V.K.; Trivedi, K.S.: Stochastic model driven capacity planning for an infrastructure-as-a-service cloud. IEEE Trans. Serv. Comput. 7(4), 667–680 (2014)

    Article  Google Scholar 

  25. Mondal, S.K.; Muppala, J.K.; Machida, F.: Virtual machine replication on achieving energy-efficiency in a cloud. Electronics 5(3), 37 (2016)

    Article  Google Scholar 

  26. Sun, G.; Liao, D.; Anand, V.; Zhao, D.; Yu, H.: A new technique for efficient live migration of multiple virtual machines. Future Gener. Comput. Syst. 55, 74–86 (2016)

    Article  Google Scholar 

  27. Cheikh, H.B.; Doncel, J.; Brun, O.; Prabhu, B.: Predicting response times of applications in virtualized environments. In: Proceedings of the 3rd Symposium on Network Cloud Computing and Applications, NCCA’14, Rome, pp. 83–90 (2014)

  28. Nguyen, T.A.; Kim, D.S.; Park, J.S.: Availability modeling and analysis of a data center for disaster tolerance. Future Gener. Comput. Syst. 56, 27–50 (2016)

    Article  Google Scholar 

  29. Salah, K.; Elbadawi, K.; Boutaba, R.: An analytical model for estimating cloud resources of elastic services. J. Netw. Syst. Manag. 24(2), 285–308 (2016)

    Article  Google Scholar 

  30. Boru, D.; Kliazovich, D.; Granelli, F.; Bouvry, P.; Zomaya, A.Y.: Energy-efficient data replication in cloud computing datacenters. Cluster Comput. 18(1), 385–402 (2015)

    Article  Google Scholar 

  31. Katz, R.H.: Tech titans building boom. IEEE Spectr. 46(2), 40–54 (2009)

    Article  Google Scholar 

  32. Salah, K.; El Kafhali, S.: Performance modeling and analysis of hypoexponential network servers. J. Telecommun. Syst. 65(4), 717–728 (2017)

    Article  Google Scholar 

  33. Vilaplana, J.; Solsona, F.; Teixido, I.; Mateo, J.; Abella, F.; Rius, J.: A queuing theory model for cloud computing. J. Supercomput. 69(1), 492–507 (2014)

    Article  Google Scholar 

  34. Bolch, G.; de Greiner, S.; Meer, H.; Trivedi, K.S.: Queueing Networks and Markov Chains: Modeling and Performance Evaluation with Computer Science Applications. Wiley, London (2006)

    Book  Google Scholar 

  35. El Kafhali, S.; Salah, K.: Efficient and dynamic scaling of fog nodes for IoT devices. J. Supercomput. 73(12), 5261–5284 (2017)

    Article  Google Scholar 

  36. Nelson, R.: Probability, Stochastic Processes, and Queueing Theory: The Mathematics of Computer Performance Modeling. Springer, Berlin (2013)

    Google Scholar 

  37. Dayarathna, M.; Wen, Y.; Fan, R.: Data center energy consumption modeling: a survey. IEEE Commun. Surv. Tutor. 18(1), 732–794 (2016)

    Article  Google Scholar 

  38. Semeraro, G.; Magklis, G.; Balasubramonian, R.; Albonesi, D.H.; Dwarkadas, S.; Scott, M.L.: Energy-efficient processor design using multiple clock domains with dynamic voltage and frequency scaling. In: Proceedings of the IEEE 8th International Symposium on High Performance Computer Architecture, HPCA’02, Cambridge, pp. 29–40 (2002)

  39. Beloglazov, A.; Buyya, R.: Energy efficient resource management in virtualized cloud data centers. In: Proceedings of the 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, CCGrid’10, Melbourne, Victoria, Australia, pp. 826–831 (2010)

  40. Radhakrishnan, A.; Kavitha, V.: Energy conservation in cloud data centers by minimizing virtual machines migration through artificial neural network. Computing 98(11), 1185–1202 (2016)

    Article  MathSciNet  Google Scholar 

  41. Gandhi, A.; Harchol-Balter, M.; Das, R.; Lefurgy, C.: Optimal power allocation in server farms. In: ACM SIGMETRICS Performance Evaluation Review, SIGMETRICS’09, vol. 37, no. 1, pp. 157–168 (2009)

  42. Kusic, D.; Kephart, J.O.; Hanson, J.E.; Kandasamy, N.; Jiang, G.: Power and performance management of virtualized computing environments via lookahead control. Clust. Comput. 12(1), 1–15 (2009)

    Article  Google Scholar 

  43. Raghavendra, R.; Ranganathan, P.; Talwar, V.; Wang, Z.; Zhu, X.: No power struggles: coordinated multi-level power management for the data center. In: ACM SIGOPS Operating Systems Review, ASPLOS’08 vol. 42, no. 2, pp. 48–59 (2008)

  44. Verma, A.; Ahuja, P.; Neogi, A.: pmapper: power and migration cost aware application placement in virtualized systems. In: Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware, Middleware’08, Leuven, Belgium, pp. 243–264 (2008)

  45. Awada, U.; Li, K.; Shen, Y.: Energy consumption in cloud computing data centers. Int. J. Cloud Comput. Serv. Sci. 3(3), 145–162 (2014)

    Google Scholar 

  46. Yeo, S.; Hossain, M.M.; Huang, J.-C.; Lee, H.-H.S.: Atac: Ambient temperature-aware capping for power efficient datacenters. In: Proceedings of the ACM Symposium on Cloud Computing, SoCC’14, Seattle, WAACM, pp. 1–14 (2014)

  47. Mazzucco, M.; Dyachuk, D.; Dikaiakos, M.: Profit-aware server allocation for green internet services. In: Proceedings of the IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication System, MASCOTS’10, Miami, Florida, USA, pp. 277–284 (2010)

  48. Gandhi, A.; Harchol-Balter, M.; Adan, I.: Server farms with setup costs. Perform. Eval. 67(11), 1123–1138 (2010)

    Article  Google Scholar 

  49. Burnetas, A.; Economou, A.: Equilibrium customer strategies in a single server markovian queue with setup times. Queueing Syst. 56(3–4), 213–228 (2007)

    Article  MathSciNet  Google Scholar 

  50. Gandhi, A.; Harchol-Balter, M.: M/g/k with exponential setup, Tech. Rep. CMU-CS-09-166, School of Computer Science, Carnegie Mellon University (2009)

  51. Nguyen, B.M.; Tran, D.; Nguyen, Q.: A strategy for server management to improve cloud service qos. In: Proceedings of the IEEE/ACM 19th International Symposium on Distributed Simulation and Real Time Applications, IEEE/ACM DS-RT’15, Chengdu, China, pp. 120–127 (2015)

  52. Mazzucco, M.; Dyachuk, D.: Balancing electricity bill and performance in server farms with setup costs. Future Gener. Comput. Syst. 28(2), 415–426 (2012)

    Article  Google Scholar 

  53. Han, Z.; Tan, H.; Chen, G.; Wang, R.; Chen, Y.; Lau, F.: Dynamic virtual machine management via approximate markov decision process. In: Proceedings of the 35th Annual IEEE International Conference on Computer Communications, IEEE INFOCOM’16, San Francisco, CA, USA, pp .1–9 (2016)

  54. Elaydi, S.: An Introduction to Difference Equations. Springer, Berlin (2005)

    MATH  Google Scholar 

  55. Bahwaireth, K.; Benkhelifa, E.; Jararweh, Y.; Tawalbeh, M.A.: Experimental comparison of simulation tools for efficient cloud and mobile cloud computing applications. EURASIP J. Inf. Secur. 2016(1), 1–14 (2016)

    Article  Google Scholar 

  56. Tian, W.; Xu, M.; Chen, A.; Li, G.; Wang, X.; Chen, Y.: Open-source simulators for cloud computing: comparative study and challenging issues. Simul. Model. Pract. Theory 58, 239–254 (2015)

    Article  Google Scholar 

  57. Fahmy, H.M.A.: Simulators and emulators for WSNs. In: Wireless Sensor Networks. Signals and Communication Technology. Springer, Berlin, pp. 381–491 (2016)

    Google Scholar 

  58. Bertoli, M.; Casale, G.; Serazzi, G.: JMT: performance engineering tools for system modeling. ACM SIGMETRICS Perform. Eval. Rev. 36(4), 10–15 (2009)

    Article  Google Scholar 

  59. Sarna, D.E.: Implementing and Developing Cloud Computing Applications. CRC Press, Boca Raton (2010)

    Book  Google Scholar 

  60. Trivedi, K.S.; Sahner, R.: Sharpe at the age of twenty two. ACM SIGMETRICS Perform. Eval. Rev. 36(4), 52–57 (2009)

    Article  Google Scholar 

  61. Sharpe. https://sharpe.pratt.duke.edu/

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El Kafhali, S., Salah, K. Modeling and Analysis of Performance and Energy Consumption in Cloud Data Centers. Arab J Sci Eng 43, 7789–7802 (2018). https://doi.org/10.1007/s13369-018-3196-0

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