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Cluster Computing

, Volume 22, Supplement 1, pp 583–596 | Cite as

Online energy-efficient deployment based on equivalent continuous DFS for large-scale web cluster

  • Zhi XiongEmail author
  • Ting Guo
  • Zhongliang Xue
  • Weihong Cai
  • Lingru Cai
  • Nanfu Luo
Article
  • 412 Downloads

Abstract

How to dynamically deploy web cluster so as to reduce energy consumption and mean-while satisfy performance requirements is an urgent problem to be resolved. In this paper, we propose an online energy-efficient deployment strategy to minimize cluster’s energy consumption on the premise of guaranteeing server’s CPU utilization equal to a given target value. It adopts CPU equivalent continuous Dynamic Frequency Scaling to reduce server power. First, we propose an approach of CPU utilization guarantee. Then, we describe cluster’s energy-efficient deployment problem as a constrained Mixed Integer Programming problem. Compared with similar works, our variable definition manner can reduce variable number significantly. Finally, we propose an improved differential evolution algorithm to solve the problem. Because of few variable number and high solving efficiency, even if applied to large-scale clusters, our strategy can still dynamically deploy the cluster online. Evaluation results verify the feasibility and effectiveness of the proposed deployment strategy.

Keywords

Web cluster Energy-efficient Dynamic frequency scaling CPU utilization Large-scale 

Notes

Acknowledgements

The authors acknowledge the Special Funds for Discipline and Specialty Construction of Guangdong Higher Education Institutions (2016KTSCX040), the Science and Technology Planning Project of Guangdong Province (No. 2016B090920095, 2016B010124012), and the Science and Technology Program of Shantou (No. 2014-98).

References

  1. 1.
    Bilal, K., Fayyaz, A., Khan, S.U., et al.: Power-aware resource allocation in computer clusters using dynamic threshold voltage scaling and dynamic voltage scaling: comparison and analysis. Clust. Comput. 18(2), 865–888 (2015)CrossRefGoogle Scholar
  2. 2.
    Batheja, J., Parashar, M.: A framework for adaptive cluster computing using JavaSpaces. Clust. Comput. 6(3), 201–213 (2003)CrossRefzbMATHGoogle Scholar
  3. 3.
    Li, K.: Power and performance management for parallel computations in clouds and data centers. J. Comput. Syst. Sci. 82(2), 174–190 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Ghamkhari, M., Mohsenian-Rad, H.: Energy and performance management of green data centers: a profit maximization approach. IEEE Trans. Smart Grid 4(2), 1017–1025 (2013)CrossRefGoogle Scholar
  5. 5.
    Valentini, G.L., Lassonde, W., Khan, S.U., et al.: An overview of energy efficiency techniques in cluster computing systems. Clust. Comput. 16(1), 3–15 (2013)CrossRefGoogle Scholar
  6. 6.
    Piga, L., Bergamaschi, R.A., Rigo, S.: Empirical and analytical approaches for web server power modeling. Clust. Comput. 17(4), 1279–1293 (2014)CrossRefGoogle Scholar
  7. 7.
    Mazumdar, S., Pranzo, M.: Power efficient server consolidation for cloud data center. Future Gener. Comput. Syst. 70, 4–16 (2017)CrossRefGoogle Scholar
  8. 8.
    Valentini, G.L.: An overview of energy efficiency techniques in cluster computing systems. Clust. Comput. 16(1), 3–15 (2013)CrossRefGoogle Scholar
  9. 9.
    Rizvandi, N.B., Taheri, J., Zomaya, A.Y.: Some observations on optimal frequency selection in DVFS-based energy consumption minimization. J. Parallel Distrib. Comput. 71(8), 1154–1164 (2011)CrossRefzbMATHGoogle Scholar
  10. 10.
    Santana, C., Leite, J.C.B., Mossé, D.: Power management by load forecasting in web server clusters. Clust. Comput. 14(4), 471–481 (2011)CrossRefGoogle Scholar
  11. 11.
    Al-Qawasmeh, A.M., Pasricha, S., Maciejewski, A.A., et al.: Power and thermal-aware workload allocation in heterogeneous data centers. IEEE Trans. Comput. 64(2), 477–491 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Gao, Y., Guan, H., Qi, Z., et al.: Service level agreement based energy-efficient resource management in cloud data centers. Comput. Electr. Eng. 40(5), 1621–1633 (2014)CrossRefGoogle Scholar
  13. 13.
    Wang, P., Qi, Y., Liu, X.: Power-aware optimization for heterogeneous multi-tier clusters. J. Parallel Distrib. Comput. 74(1), 2005–2015 (2014)CrossRefGoogle Scholar
  14. 14.
    Shi, X., Dong, J., Djouadi, S.M., et al.: PAPMSC: power-aware performance management approach for virtualized web servers via stochastic control. J. Grid Comput. 14(1), 171–191 (2016)CrossRefGoogle Scholar
  15. 15.
    Deng, Y., Hu, Y., Meng, X., et al.: Predictively booting nodes to minimize performance degradation of a power-aware web cluster. Clust. Comput. 17(4), 1309–1322 (2014)CrossRefGoogle Scholar
  16. 16.
    Gandhi, A., Chen, Y., Gmach, D., et al.: Hybrid resource provisioning for minimizing data center SLA violations and power consumption. Sustain. Comput. 2(2), 91–104 (2012)Google Scholar
  17. 17.
    Piga, L., Bergamaschi, R.A., Breternitz, M., et al.: Adaptive global power optimization for web servers. J. Supercomput. 68(3), 1088–1112 (2014)CrossRefGoogle Scholar
  18. 18.
    Cao, J., Li, K., Stojmenovic, I.: Optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers. IEEE Trans. Comput. 63(1), 45–58 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Song, J., Li, T., Yan, Z., et al.: Energy-efficiency model and measuring approach for cloud computing. J. Softw. 23(2), 200–214 (2012)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Ali, M.M., Zhu, W.X.: A penalty function-based differential evolution algorithm for constrained global optimization. Comput. Optim. Appl. 54(3), 707–739 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Liu, J., Teo, K.L., Wang, X., et al.: An exact penalty function-based differential search algorithm for constrained global optimization. Soft Comput. 20(4), 1305–1313 (2016)CrossRefGoogle Scholar
  22. 22.
    Dong, N., Wang, Y.: Guiding multi-objective differential evolution algorithm for constrained optimization. J. Jilin Univ. Eng. Technol. Ed. 45(2), 569–575 (2015)Google Scholar
  23. 23.
    Dayarathna, M., Wen, Y., Fan, R.: Data center energy consumption modeling: a survey. IEEE Commun. Surv. Tutor. 18(1), 732–794 (2016)CrossRefGoogle Scholar
  24. 24.
    Entrialgo, J., Medrano, R., García, D.F., et al.: Autonomic power management with self-healing in server clusters under QoS constraints. Computing 98(9), 871–894 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Chandnani, L., Kapoor, H.K.: Formal approach for DVS-based power management for multiple server system in presence of server failure and repair. IEEE Trans. Ind. Inform. 9(1), 502–513 (2013)CrossRefGoogle Scholar
  26. 26.
    Kuehn, P.J., Mashaly, M.E.: Automatic energy efficiency management of data center resources by load-dependent server activation and sleep modes. Ad Hoc Netw. 25, 497–504 (2015)CrossRefGoogle Scholar
  27. 27.
    Cheng, D., Guo, Y., Jiang, C., et al.: Self-tuning batching with DVFS for performance improvement and energy efficiency in Internet servers. ACM Trans. Auton. Adapt. Syst. 10(1), 1–32 (2015)CrossRefGoogle Scholar
  28. 28.
    Sousa, L.S., Leite, J.C.B., Loques, O.: Green data centers: Using hierarchies for scalable energy efficiency in large web clusters. Inf. Process. Lett. 113(14–16), 507–515 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Kim, J., Chou, J., Rotem, D.: iPACS: power-aware covering sets for energy proportionality and performance in data parallel computing clusters. J. Parallel Distrib. Comput. 74(1), 1762–1774 (2014)CrossRefGoogle Scholar
  30. 30.
    Enokido, T., Duolikun, D., Takizawa, M.: An energy-aware load balancing algorithm to perform computation type application processes in a cluster of servers. Int. J. Web Grid Serv. 13(2), 145–169 (2017)Google Scholar
  31. 31.
    Zhao, X., Peng, T., Qin, X., et al.: Feedback control scheduling in energy-efficient and thermal-aware data centers. IEEE Trans. Syst. Man Cybern. Syst. 46(1), 48–60 (2016)CrossRefGoogle Scholar
  32. 32.
    AL-Hazemi, F., Kang, D.K., Kim, S.H., et al.: LPCFreqSchd: a local power controller using the frequency scheduling approach for virtualized servers. Clust. Comput. 19(2), 663–678 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyShantou UniversityShantouChina

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