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Air Flow Based Failure Model for Data Centers

  • Hao Feng
  • Yuhui Deng
  • Liang Yu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11334)

Abstract

With the explosive growth of data, thousands upon thousands servers are contained in data centers. Hence, node failure is unavoidable and it generally brings effects on the performance of the whole data center. On the other hand, data centers with vast nodes will cause plenty of energy consumption. Many existing task scheduling techniques can effectively reduce the power consumption in data centers by considering heat recirculation. However, traditional techniques barely take the situation of node failure into account. This paper proposes an airflow-based failure model for data centers by leveraging heat recirculation. In this model, the spatial distribution and time distribution of failure nodes are considered. Furthermore, the Genetic algorithm (GA) and Simulated Annealing algorithm (SA) are implemented to evaluate the proposed failure model. Because the position of failures has a significant impact on the heat recirculation and the energy consumption of data centers, failure nodes with different positions are analyzed and evaluated. The experimental results demonstrate that the energy consumption of data centers can be significantly reduced by using the GA and SA algorithms for task scheduling based on proposed failure model.

Keywords

Energy efficiency Data centers Node failure Task schedule 

Notes

Acknowledgements

This work is supported by the NSFC (no.61572232), in part by the Science and Technology Planning Project of Guangzhou (no. 201802010028, and no. 201802010058), in part by the Science and Technology Planning Project of Nansha (no. 2017CX006), and in part by the Open Research Fund of Key Laboratory of Computer System and Architecture, Institute of Computing Technology, Chinese Academy of Sciences under Grant CARCH201705.

References

  1. 1.
    Bilal, K., Malik, S.U.R., Khan, S.U., Zomaya, A.Y.: Trends and challenges in cloud datacenters. IEEE Cloud Comput. 1(1), 10–20 (2014)CrossRefGoogle Scholar
  2. 2.
    Cheng, Y., Fiorani, M., Wosinska, L., Chen, J.: Reliable and cost efficient passive optical interconnects for data centers. IEEE Commun. Lett. 19(11), 1913–1916 (2015)CrossRefGoogle Scholar
  3. 3.
    Deng, Y.: What is the future of disk drives, death or rebirth? ACM Comput. Surv. 43(3), 1–27 (2011)CrossRefGoogle Scholar
  4. 4.
    Deng, Y., Hu, Y., Meng, X., Zhu, Y., Zhang, Z., Han, J.: Predictively booting nodes to minimize performance degradation of a power-aware web cluster. Cluster Comput. 17, 1309–1322 (2014)CrossRefGoogle Scholar
  5. 5.
    Deng, Y., Huang, X., Song, L., Zhou, Y., Wang, F.: Memory deduplication: an effective approach to improve the memory system. J. Inf. Sci. Eng. 33, 1103–1120 (2017)Google Scholar
  6. 6.
    Elgelany, A.: Energy efficiency for data centers and cloud computing: a literature review. Energy 3 (2013)Google Scholar
  7. 7.
    Fan, X., Weber, W.D., Barroso, L.A.: Power provisioning for a warehouse-sized computer. ACM (2007)Google Scholar
  8. 8.
    Ferreira, A.M., Pernici, B.: Managing the complex data center environment: an integrated energy-aware framework. Compute 98, 709–749 (2016)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Guitart, J.: Toward sustainable data centers: a comprehensive energy management strategy. Computing 99(6), 597–615 (2017)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Hua, Y., Liu, X., Jiang, H.: Antelope: a semantic-aware data cube scheme for cloud data center networks. IEEE Trans. Comput. 63(9), 2146–2159 (2014)MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Lee, Y.C., Zomaya, A.Y.: Energy efficient utilization of resources in cloud computing systems. J. Supercomput. 60(2), 268–280 (2012)CrossRefGoogle Scholar
  12. 12.
    Li, H., Zhu, G., Cui, C., Tang, H., Dou, Y., He, C.: Energy-efficient migration and consolidation algorithm of virtual machines in data centers for cloud computing. Computing 98(3), 303–317 (2016)MathSciNetzbMATHCrossRefGoogle Scholar
  13. 13.
    Li, L., Ho, D.W.C., Lu, J.: A consensus recovery approach to nonlinear multi-agent system under node failure (2016)Google Scholar
  14. 14.
    Lin, R., Deng, Y.: Allocating workload to minimize the power consumption of data centers. Front. Comput. Sci. 11(1), 105–118 (2017)CrossRefGoogle Scholar
  15. 15.
    Liu, Z., et al.: Renewable and cooling aware workload management for sustainable data centers. In: ACM Sigmetrics/Performance Joint International Conference on Measurement and Modeling of Computer Systems, pp. 175–186 (2012)Google Scholar
  16. 16.
    Moore, J., Chase, J., Ranganathan, P., Sharma, R.: Making scheduling “cool": temperature-aware workload placement in data centers. In: Usenix Technical Conference, Anaheim, CA, USA, 10–15 April 2005, pp. 61–75 (2008)Google Scholar
  17. 17.
    Polverini, M., Vasilakos, A.V., Ren, S., Cianfrani, A.: Thermal-aware scheduling of batch jobs in geographically distributed data centers. IEEE Trans. Cloud Comput. 2(1), 71–84 (2014)CrossRefGoogle Scholar
  18. 18.
    Popoola, O., Pranggono, B.: On energy consumption of switch-centric data center networks. J. Supercomput. 1–36 (2017)Google Scholar
  19. 19.
    Sahoo, R.K., Sivasubramaniam, A., Squillante, M.S., Zhang, Y.: Failure data analysis of a large-scale heterogeneous server environment, p. 772 (2004)Google Scholar
  20. 20.
    Sanjeevi, P., Viswanathan, P.: Nuts scheduling approach for cloud data centers to optimize energy consumption. Computing 11, 1–27 (2017)Google Scholar
  21. 21.
    Schroeder, B., Gibson, G.A.: A large-scale study of failures in high-performance computing systems. IEEE Trans. Dependable Secure Comput. 7(4), 337–350 (2010)CrossRefGoogle Scholar
  22. 22.
    Tang, Q., Gupta, S.K.S., Stanzione, D., Cayton, P.: Thermal-aware task scheduling to minimize energy usage of blade server based datacenters. In: IEEE International Symposium on Dependable, Autonomic and Secure Computing, pp. 195–202 (2006)Google Scholar
  23. 23.
    Tang, Q., Gupta, S.K.S., Varsamopoulos, G.: Energy-efficient thermal-aware task scheduling for homogeneous high-performance computing data centers: a cyber-physical approach. IEEE Trans. Parallel Distrib. Syst. 19(11), 1458–1472 (2008)CrossRefGoogle Scholar
  24. 24.
    Wang, L., Khan, S.U.: Review of performance metrics for green data centers: a taxonomy study. J. Supercomput. 63(3), 639–656 (2013)CrossRefGoogle Scholar
  25. 25.
    Wang, L., Khan, S.U., Dayal, J.: Thermal aware workload placement with task-temperature profiles in a data center. J. Supercomput. 61(3), 780–803 (2012)CrossRefGoogle Scholar
  26. 26.
    Wei, J., Jiang, H., Zhou, K., Feng, D.: Efficiently representing membershipfor variable large data sets. IEEE Trans. Parallel Distrib. Syst. 25(4), 960–970 (2014)CrossRefGoogle Scholar
  27. 27.
    Wierman, A., Andrew, L.L.H., Thereska, E.: Dynamic right-sizing for power-proportional data centers. IEEE/ACM Trans. Networking 21(5), 1378–1391 (2011)Google Scholar
  28. 28.
    Xie, J., Deng, Y., Min, G., Zhou, Y.: An incrementally scalable and cost-efficient interconnection structure for data centers. IEEE Trans. Parallel Distrib. Syst. 28(6), 1578–1592 (2017)CrossRefGoogle Scholar
  29. 29.
    Yang, L., Deng, Y., Yang, L.T., Lin, R.: Reducing the cooling power of data centers by intelligently assigning tasks. IEEE Internet Things J. 5(3), 1667–1678 (2017)CrossRefGoogle Scholar
  30. 30.
    Zhan, X., Reda, S.: Power budgeting techniques for data centers. IEEE Trans. Comput. 64(8), 2267–2278 (2015)MathSciNetzbMATHCrossRefGoogle Scholar
  31. 31.
    Zhang, L., Deng, Y., Zhu, W., Zhou, J., Wang, F.: Skewly replicating hot data to construct a power-efficient storage cluster. J. Netw. Comput. Appl. 50, 168–179 (2015)CrossRefGoogle Scholar
  32. 32.
    Zhang, Y., Squillante, M.S., Sivasubramaniam, A., Sahoo, R.K.: Performance implications of failures in large-scale cluster scheduling. In: Feitelson, D.G., Rudolph, L., Schwiegelshohn, U. (eds.) JSSPP 2004. LNCS, vol. 3277, pp. 233–252. Springer, Heidelberg (2005).  https://doi.org/10.1007/11407522_13CrossRefGoogle Scholar
  33. 33.
    Zhou, K., Hu, S., Huang, P.H., Zhao, Y.: LX-SSD : enhancing the lifespan of NAND flash-based memory via recycling invalid pages. In: 33rd International Conference on Massive Storage Systems and Technology (MSST 2017) (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Jinan UniversityGuangdongChina
  2. 2.The State Key Laboratory of Computer Architecture, Institute of Computing TechnologyChinese Academy of SciencesBeijingChina

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